Revisiting the Importance ofIndividual Units in CNNs via Ablation

Bolei ZhouMIT

Yiyou SunHarvard

David BauMIT

Antonio TorralbaMIT

Abstract

We revisit the importance of the individual units in Convolutional Neural Net-works (CNNs) for visual recognition. By conducting unit ablation experiments onCNNs trained on large scale image datasets, we demonstrate that, though ablatingany individual unit does not hurt overall classification accuracy, it does lead tosignificant damage on the accuracy of specific classes. This result shows that anindividual unit is specialized to encode information relevant to a subset of classes.We compute the correlation between the accuracy drop under unit ablation andvarious attributes of an individual unit such as class selectivity and weight L1 norm.We confirm that unit attributes such as class selectivity are a poor predictor forimpact on overall accuracy as found previously in recent work [11]. However, ourresults show that class selectivity along with other attributes are good predictors ofthe importance of one unit to individual classes. We evaluate the impact of randomrotation, batch normalization, and dropout to the importance of units to specificclasses. Our results show that units with high selectivity play an important rolein network classification power at the individual class level. Understanding andinterpreting the behavior of these units is necessary and meaningful.

1 Introduction

Attempts to understand the internal mechanisms of deep neural networks have frequently boileddown to analyzing the role of individual units. Previous work has visualized the units of deepconvolutional networks by sampling image patches that maximize activation of each units [20, 22, 3]or by generating images that maximize each unit activation [12]. Such visualizations show thatindividual units act as visual concept detectors. Units at lower layers detect concrete patterns suchas textures or shapes while units at high layers detect more semantically meaningful concepts suchas dog heads or bicycle wheels. The interpretability of individual units has been quantified [1] bymeasuring the degree of alignment between their activations and human annotated concepts. However,though the units are shown to become more selective and more semantically meaningful in higherlayers, how they contribute to the final prediction has not been analyzed in detail.

A very interesting recent paper [11] has used unit ablation experiments to quantify the importance ofthe units to the network output. The paper has shown that class selectivity is a poor predictor of aunit’s importance towards overall accuracy of the classification task. These results suggest that ‘highlyclass selective units may be harmful to the network performance’ implying that ‘understanding neuralnetworks based on analyzing highly selective single units or finding optimal inputs for single units,may be misleading.’ That conclusion seems to go against previous work on single unit visualization,opening a debate on the importance of understanding individual units in CNNs. In this paper we willshow that both results are compatible. We have conducted a detailed analysis on the effect of ablation,showing that a different story is revealed when comparing the importance of highly selective units totheir contribution to overall accuracy versus specific class accuracy.

For example, Figure 1 shows how ablating one unit has a severe impact on the accuracy in specificclasses while showing only minimal effects on overall accuracy. For instance, if we remove the unit

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overall accuracy: 50.6%overall accuracy after ablating unit 115: 50.4%

youth_hostelbedroombedchamberchilds_roomhotel_room

Figure 1: Ablating a unit which is selective to bed in images greatly damages the recognition ofbed-relevant classes. We show 9 top activated images segmented by the feature activation on unit 115from conv5 in AlexNet trained for place recognition. Ablating this unit from the network leads toa very small effect on the overall performance of the network (when classifying 365 place classes,it goes from 50.6% accuracy to 50.4% accuracy after ablating the unit.) Despite of the small drop(0.2%) in overall accuracy, the drop in class accuracy is not uniform across all classes. As shownin the plot of sorted drop in class accuracy, the drop is significant (> 10%) in some classes suchas youth hostel and bedroom for which detecting beds is important. Image samples from the top 3damaged classes are shown below.

which is selective to ‘beds’ the overall performance does not drop much. However the accuracy forrecognizing the youth hostel class drops by 23% while the one for bedroom class drops by 15%.That is a huge drop in performance for a single class given that we are only removing one unit. Asthere are 365 categories, doing badly on a single category (or a handful) might have almost no impacton the overall performance, but the classifier will perform really bad on some classes.

The current work shows that units with high selectivity in a network are crucial for recognizingspecific classes. We confirm that when ablating an individual unit, the damage to generalizationaccuracy averaged over all classes is small: this is consistent with the findings in [11]. But we furtherfind that ablating a single unit tends to cause significant damage to generalization accuracy on asubset of classes. We show that the class selectivity as well as other attributes are a reasonably goodpredictors for measuring the importance of the individual units to specific classification accuracy, andthat understanding and interpreting the behaviors of individual units is meaningful.

1.1 Related Work

Investigation of deep network internals can be summarized at three major levels of detail as follows.

Whole networks have been studied to characterize their learning ability. It has been demonstrated[21] that the same classification networks that successfully generalize meaningful labels are alsoable to memorize data with meaningless random labels. Conversely, it has been observed [18] thatnetworks are susceptible to being fooled on tiny perturbations of test inputs. These observationshave challenged traditional assumptions about what a network learns, and why. Our current workcontributes to the effort to characterize the mechanisms deep networks use to achieve generalization.

Networks have also have been studied at the granularity of individual layers. It has been found thatthe representations produced by a layer summarize the lessons learned in training in a meaningfulway [19]: low-level layers can be used to summarize simpler patterns, and higher-level layers encodemore abstract representations. Layers trained on one problem can be reused to transfer knowledgeto other problems [14], with a small amount of additional training. This fact has profound practicalimportance, reducing the need to build separate large training sets for every problem.

Finally, the role of individual network units has been studied. It has been found that individualunits are sensitive to specific concepts that can be visualized by generating or sampling inputs thatmaximize unit activations [22, 12], and that unit directions match meaningful concepts more closelythan other random directions in representation space [1]. However, whether meaningful units are

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helpful or harmful to a network’s ability to generalize has been questioned: [11] found that unitsthat are selective to one class do not appear to damage overall classification performance more thanother units when removed from a network. Our current work is a further examination of the impactof individual units on generalization accuracy: we ask, what is the role of a single unit in contributingto classification performance, and what characteristics of units are predictive of their contribution?

2 Measuring the Importance of Units for Classification

Given the human level visual recognition performed by deep neural network, without doubt eachindividual unit in the network plays a role in recognizing the image. However the importance ofindividual units on overall classification accuracy is not as well analyzed or quantified. In this sectionwe measure the importance of units for classification through unit ablation and then characterize therelationship to several other relevant attributes.

2.1 Classification Accuracy Drop from Unit Ablation

To quantify the importance of an individual unit for the network’s classification accuracy, we conductunit ablation experiments with a similar setup as that used in [11]. We ablate one unit then computethe resulting classification accuracy drop. This provides a way to quantify the importance of unit inthe classification. Note that for all experiments we measure generalization accuracy, which is theclassification accuracy on a held-out validation set, rather than accuracy on the training set.

We ablate a unit as follows: For each unit, we set its weight and bias as 0 so that it will not contributeto the prediction for any input image. We pass the images from the validation set into the networkwith one unit ablated at a time, then compute the generalization accuracy drop.

In our analysis, we measure two types of accuracy drop: the first one is the overall accuracy dropwhich is the total percentage of images misclassified due to ablating the unit. The second one is classaccuracy drop which measures the difference in accuracy in correctly classifying images of eachindividual class due to ablating the unit. If the number of testing samples per class is the same, whichis the case for both ImageNet [15] and Places [23], then the overall accuracy drop is simply averageof the class accuracy drops. Note that the class accuracy drop is a vector with dimension equal tothe number of classes. We can summarize this vector by its maximum value, which is the max classaccuracy drop. The max class accuracy drop measures the importance of unit to predictions ofinstances of an individual class. We can further sort units in a layer by their max class accuracy dropto have max class accuracy drop curve, as a measure for the degree of class-specific informationcarried by individual units. We will show later in Fig.7 and Fig.8, the more flat the curve is, the lessclass-specific information carried by individual units, therefore units will be less in single meaningfuldirections and less interpretable in a layer.

In later experiments, we will build a model to predict the overall accuracy drop and max classaccuracy drop based on other observable characteristics of a unit. We will show that although ablatingan individual unit does not tend to cause a large drop in overall accuracy, it does lead to significantloss in accuracy for a subset of classes. This suggests that individual units align with single directionsin representation space that carry information that is specialized to a subset of classes.

2.2 Characterizing the Individual Units with Attributes

To further understand the reasons for a unit’s importance to class accuracy and overall generalizationaccuracy, we study several other attributes to characterize each individual unit. We would like tounderstand how these attributes of units may be correlated with or predictive of the unit’s importancein classification.

In various different contexts in the literature, several metrics have been used to quantify the propertiesof individual units: for example class selectivity [11], unit correlation [10], the alignment betweenunit activation and visual concepts used in network dissection [1], or L1 norm of the filter weightwhich is commonly used in network compression [9, 5]. We summarize each attribute as follows:

L1 Norm. This number is simply the L1 norm of the learned weights of the unit. Despite of thesimplicity of the metric, it has been shown to be an effective metric for pruning units in network

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compression [4]. The L1 norm of the ith unit is

norm1(i) = ||wi||1 =∑j

|(wi)j | (1)

Class Correlation. As shown in [10], the statistical correlation of unit activations reflects how relatedtwo units’ representations of images are. So that we can use the correlation between the activation ofunit i and the predicted probability for class k as the amount of information carried by the unit. Theclass correlation is computed as

corr(i, k) =E[(xi − x̄i)(pk − p̄k)]

σxiσpk

(2)

where xi denotes the ith unit’s activations, pk denotes the kth class’s predicted probability for thesame set of input images, and x̄i and p̄k denote mean values while σxi

and σpkdenote the standard

deviation. Note that for each unit, the class correlation is a vector of dimension equal to the numberof classes. We can take the maximum value of the vector as the scalar class correlation.

Class Selectivity. The class selectivity is a metric for measuring the single direction of units used in[11]. It is calculated as follows,

select(i, k) =x̄ki − x̄

−ki

x̄ki + x̄−ki

(3)

where x̄ki denotes the mean activation value of unit i when the input image belong to class k and x̄−ki

denotes the mean activation value of unit i averaged across all other classes (non kth classes). Referto [11] for detailed description of the metric. The class selectivity is also a vector of dimension equalto the number of classes. We can take the maximum value of the vector as the scalar class selectivityas used in [11]. This metric varies from 0 to 1, with 1 indicating the unit active for inputs of a singleclass and 0 indicating the unit active identically for all classes.

Concept Alignment. In [1], the interpretability of units is measured as the IoU (Intersection overUnion) score, the degree of alignment between unit activation and the ground-truth semantic maskcontaining 1198 visual concepts. For each unit i, we can compute the IoU for each one of 1198concepts used in [1] as the Concept IoU attribute of the unit. Taking the maximum of the IoU and itsassociated label, we get a scalar concept IoU of that unit as used in [1].

Unit Visualization. Visualization of units can be also considered as a qualitative attribute that canreveal which image patterns are represented by the unit. To visualize a unit we generate the topactivated images following [22, 1, 2] by showing the images in the validation set that most activatethe unit. These images are further segmented by applying a binarized activation map to highlight themost activated image regions. Though there are other way to visualize unit such as back-propagation[16, 13] or image synthesis [12], we find this instance-based visualization intuitive.

We characterize the individual units using the attributes above. There are other statistical propertiesthat can be used to characterize unit activation, such as the sparsity, mean, and standard deviation ofthe activation distributions. In Sec.3.3 we analyze the relation between the importance of units inclassification and the attributes described above. We will further show that some of the attributes area good predictors of unit importance.

3 Experiments

3.1 Models and Datasets

For the following experiments, we use AlexNet [8] and ResNet18 [6] trained from scratch onImageNet [15] and Places [23] as the testing models. ImageNet has 1.2 million training images from1,000 object classes while Places has 1.6 million training images from 365 scene classes. For thevalidation set used to compute the accuracy drop from unit ablation, there are 50 images per class inImageNet and 200 images per class in Places.

3.2 Influence of Unit Ablation to Accuracy Drop

For each ablated unit, we compute the resulting overall accuracy drop and class accuracy drop.Some examples of unit ablations are shown in Fig.2. In this figure, we sort all the classes by class

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ntnu

rser

yliv

ing_

room

lect

ure_

room

libra

ry/o

utdo

orice

_cre

am_p

arlo

roa

st_h

ouse

hunt

ing_

lodg

e/ou

tdoo

rin

dust

rial_a

rea

mov

ie_t

heat

er/in

door

mot

elisl

etla

ndin

g_de

cklo

adin

g_do

cklo

bby

lock

_cha

mbe

rla

undr

omat

lock

er_r

oom

kenn

el/o

utdo

orje

welry

_sho

pm

ansio

nm

anuf

actu

red_

hom

ele

gisla

tive_

cham

ber

mar

ket/o

utdo

orlig

htho

use

med

ina

jail_

cell

mou

ntai

n_pa

thja

cuzz

i/ind

oor

inn/

outd

oor

offic

e_bu

ildin

gai

rfiel

doi

lrig

stad

ium

/bas

ebal

lst

adiu

m/fo

otba

llst

age/

indo

orst

age/

outd

oor

stai

rcas

esu

bway

_sta

tion/

plat

form

swim

min

g_po

ol/in

door

tele

visio

n_st

udio

tem

ple/

asia

towe

rto

ysho

ptra

in_in

terio

rtra

in_s

tatio

n/pl

atfo

rmtre

e_ho

use

tund

raun

derw

ater

/oce

an_d

eep

utilit

y_ro

omva

lley

vege

tabl

e_ga

rden

vete

rinar

ians

_offi

cevo

lleyb

all_c

ourt/

outd

oor

wate

r_pa

rkwe

t_ba

rwh

eat_

field

wind

_far

mwi

ndm

illya

rdso

ccer

_fie

ldof

fice_

cubi

cles

ski_r

esor

tsc

ienc

e_m

useu

mop

erat

ing_

room

orch

ard

orch

estra

_pit

pago

dapa

lace

park

phar

mac

yph

one_

boot

hph

ysics

_labo

rato

rypi

erpi

zzer

iapl

aygr

ound

hous

era

ceco

urse

race

way

raft

railr

oad_

track

rece

ptio

nre

crea

tion_

room

repa

ir_sh

opre

stau

rant

_kitc

hen

rest

aura

nt_p

atio

rice_

padd

yriv

erro

ck_a

rch

ruin

sand

box

serv

er_r

oom

pub/

indo

orho

tel_r

oom

dorm

_roo

mcla

ssro

omcle

an_r

oom cliff

coas

tco

ckpi

tco

ffee_

shop

com

pute

r_ro

om barn

corn

_fie

ld bar

base

ball_

field

banq

uet_

hall

cour

tyar

dde

partm

ent_

stor

ede

sert/

sand

dese

rt/ve

geta

tion

dese

rt_ro

addi

ner/o

utdo

ordi

ning

_hal

ldo

wnto

wnba

ll_pi

tba

dlan

dsco

ttage

auto

_sho

wroo

mho

tel/o

utdo

orba

sem

ent

boat

_dec

kbo

atho

use

book

stor

ebo

wlin

g_al

ley

boxi

ng_r

ing

brid

gebe

ach

bullr

ing

buria

l_cha

mbe

rba

zaar

/out

door

chur

ch/in

door

bus_

inte

rior

bath

room

butte

cabi

n/ou

tdoo

rca

fete

riaca

nal/u

rban

cany

onca

r_in

terio

rca

rrous

elba

sket

ball_

cour

t/ind

oor

chem

istry

_lab

bus_

stat

ion/

indo

orau

to_f

acto

rybe

droo

mfo

rest

_roa

dha

rdwa

re_s

tore

apar

tmen

t_bu

ildin

g/ou

tdoo

raq

uedu

ctgy

mna

sium

/indo

orfir

e_st

atio

nfo

otba

ll_fie

ldam

usem

ent_

arca

dear

chem

bass

ygi

ft_sh

opga

lley

gara

ge/in

door

gara

ge/o

utdo

orga

s_st

atio

ngr

eenh

ouse

/indo

orar

t_ga

llery

aren

a/ro

deo

alle

yho

spita

l_roo

mau

dito

rium

entra

nce_

hall

atriu

m/p

ublic

hosp

ital

airp

lane

_cab

inai

rpor

t_te

rmin

alal

cove

helip

ort

gene

ral_s

tore

/indo

orex

cava

tion

high

way

athl

etic_

field

/out

door

artis

ts_lo

ftfa

bric_

stor

ear

t_sc

hool

offic

egl

acie

rra

info

rest

picn

ic_ar

easw

imm

ing_

pool

/out

door

grot

tocr

evas

seel

evat

or_s

haft

esca

lato

r/ind

oor

syna

gogu

e/ou

tdoo

rpa

rkin

g_ga

rage

/out

door

moa

t/wat

erar

cade

drug

stor

elib

rary

/indo

orfo

rest

_pat

hja

pane

se_g

arde

nvi

llage

chal

etm

auso

leum

roof

_gar

den

kasb

ahst

reet

wate

r_to

wer

fire_

esca

peze

n_ga

rden

supe

rmar

ket

delic

ates

sen

shop

ping

_mal

l/ind

oor

kitc

hen

elev

ator

_lobb

yla

ke/n

atur

alvi

neya

rdba

mbo

o_fo

rest

bedc

ham

ber

prom

enad

eice

_ska

ting_

rink/

indo

orha

ngar

/out

door

mar

tial_a

rts_g

ymbe

rthm

usic_

stud

iopa

rkin

g_lo

tpa

vilio

nbu

ildin

g_fa

cade

mou

ntai

nm

useu

m/o

utdo

oras

sem

bly_

line

hom

e_of

fice

harb

orfa

stfo

od_r

esta

uran

tm

arke

t/ind

oor

patio

boar

dwal

kfo

rest

/bro

adle

af sky

corra

lfie

ld/c

ultiv

ated

amph

ithea

ter

natu

ral_h

istor

y_m

useu

mfie

ld/w

ildm

arsh

flea_

mar

ket/i

ndoo

rch

urch

/out

door

elev

ator

/doo

rar

ena/

hock

eym

osqu

e/ou

tdoo

rat

ticyo

uth_

host

elha

yfie

ldge

nera

l_sto

re/o

utdo

orco

nfer

ence

_roo

msn

owfie

ldte

levi

sion_

room

arm

y_ba

seba

nk_v

ault

play

room

barn

door

child

s_ro

omice

_flo

ear

chae

logi

cal_e

xcav

atio

nbe

ach_

hous

eoc

ean

shoe

_sho

pbu

tche

rs_s

hop

land

fill

skys

crap

erst

orag

e_ro

ompa

rkin

g_ga

rage

/indo

orba

lcony

/inte

rior

rope

_brid

geto

piar

y_ga

rden

hom

e_th

eate

rtre

nch

arch

ive

swam

pca

taco

mb

-50%

-40%

-30%

-20%

-10%

0%

10%

Clas

s Acc

urac

y Dr

op

overall accuracy: 50.6%overall accuracy after ablating unit 227: 50.4%

waterfallfountainhot_springcanal/naturalbeauty_salon

wind

mill

helip

ort

wind

_far

mst

age/

indo

oror

ches

tra_p

itph

ysics

_labo

rato

ryba

llroo

mm

usic_

stud

ioel

evat

or_lo

bby

syna

gogu

e/ou

tdoo

rar

tists

_loft

towe

rsh

ower

art_

scho

olha

ngar

/indo

orbe

auty

_sal

onst

age/

outd

oor

land

ing_

deck

swam

pki

nder

gard

en_c

lass

room

cons

truct

ion_

site

roof

_gar

den

stad

ium

/foot

ball

offic

eho

me_

offic

eph

arm

acy

brid

geoa

st_h

ouse

aren

a/ro

deo

cana

l/nat

ural

audi

toriu

mga

rage

/out

door

close

tka

sbah

elev

ator

/doo

rsk

i_res

ort

emba

ssy

food

_cou

rtpi

erin

dust

rial_a

rea

field

/wild

aren

a/pe

rform

ance

gas_

stat

ion

yout

h_ho

stel

base

ball_

field

pizz

eria

fire_

esca

peice

_she

lfre

stau

rant

hous

ela

goon

vete

rinar

ians

_offi

cedi

ning

_roo

mm

artia

l_arts

_gym

med

ina

saun

afa

rmho

spita

l_roo

mst

able

rain

fore

stto

ysho

phu

ntin

g_lo

dge/

outd

oor

cam

psite

bedr

oom

resid

entia

l_nei

ghbo

rhoo

dnu

rsin

g_ho

me

stai

rcas

eof

fice_

cubi

cles

conf

eren

ce_c

ente

rdi

ning

_hal

lpa

rkch

urch

/out

door

biol

ogy_

labo

rato

rym

useu

m/in

door

scie

nce_

mus

eum

hom

e_th

eate

rai

rpor

t_te

rmin

alna

tura

l_hist

ory_

mus

eum

sush

i_bar

galle

ycle

an_r

oom

man

ufac

ture

d_ho

me

rest

aura

nt_k

itche

ngr

eenh

ouse

/out

door

corn

_fie

ldke

nnel

/out

door

elev

ator

_sha

ftdi

scot

hequ

etu

ndra

race

way

boot

h/in

door

mos

que/

outd

oor

thro

ne_r

oom

chem

istry

_lab

vege

tabl

e_ga

rden

light

hous

etra

in_s

tatio

n/pl

atfo

rmva

lley

tree_

farm

mau

sole

umliv

ing_

room

trenc

hlo

adin

g_do

cklo

bby

lock

_cha

mbe

rm

ansio

nvi

aduc

tun

derw

ater

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an_d

eep

mar

ket/i

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rm

arke

t/out

door

mar

shlib

rary

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orre

stau

rant

_pat

iole

ctur

e_ro

omice

_ska

ting_

rink/

outd

oor

yard

whea

t_fie

ldwe

t_ba

rwa

vein

n/ou

tdoo

rwa

terin

g_ho

leisl

etwa

terfa

llwa

ter_

towe

rle

gisla

tive_

cham

ber

jacu

zzi/i

ndoo

rja

il_ce

llja

pane

se_g

arde

nje

welry

_sho

pwa

iting

_roo

mvo

lleyb

all_c

ourt/

outd

oor

volca

novi

neya

rdla

ke/n

atur

alm

ezza

nine

lawn

wate

r_pa

rkvi

llage

mou

ntai

n_pa

thm

otel

phon

e_bo

oth

shop

ping

_mal

l/ind

oor

shop

front

shed

serv

er_r

oom

scho

olho

use

play

grou

ndpl

ayro

ompo

ndpo

rch

sand

box

race

cour

seice

_ska

ting_

rink/

indo

orru

nway raft

railr

oad_

track

rope

_brid

gere

cept

ion

repa

ir_sh

opro

ck_a

rch

river

patio

train

_inte

rior

park

ing_

gara

ge/o

utdo

orsk

yscr

aper

mou

ntai

nric

e_pa

ddy

mou

ntai

n_sn

owy

ticke

t_bo

oth

tem

ple/

asia

tele

visio

n_ro

omsw

imm

ing_

pool

/out

door

swim

min

g_ho

lesu

perm

arke

tsu

bway

_sta

tion/

plat

form

ocea

nst

reet

stor

age_

room

stad

ium

/soc

cer

offic

e_bu

ildin

gst

adiu

m/b

aseb

all

orch

ard

snow

field

pago

dapa

lace

pant

rypa

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g_ga

rage

/indo

orho

tel_r

oom

zen_

gard

encla

ssro

omba

mbo

o_fo

rest

cem

eter

yba

lcony

/inte

rior

ice_f

loe

cliff

balco

ny/e

xter

ior

badl

ands

cloth

ing_

stor

eco

ast

cock

pit

coffe

e_sh

opco

mpu

ter_

room

conf

eren

ce_r

oom

auto

_sho

wroo

mco

rral

cotta

geco

urth

ouse

auto

_fac

tory

atriu

m/p

ublic

cour

tyar

dcr

eek

crev

asse

beac

h_ho

use

delic

ates

sen

depa

rtmen

t_st

ore

cata

com

bat

hlet

ic_fie

ld/o

utdo

orca

stle

car_

inte

rior

bedc

ham

ber

beer

_gar

den

beer

_hal

lbe

rthba

zaar

/out

door

bask

etba

ll_co

urt/i

ndoo

rbo

okst

ore

barn

door

bota

nica

l_gar

den

bowl

ing_

alle

ybo

xing

_rin

gba

rnbu

ildin

g_fa

cade

bullr

ing

buria

l_cha

mbe

rba

rbu

s_st

atio

n/in

door

butc

hers

_sho

pbu

tteba

nque

t_ha

llca

bin/

outd

oor

cafe

teria

cam

pus

cand

y_st

ore

cany

onca

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elas

sem

bly_

line

beac

hfo

rest

_pat

hhi

ghwa

yfis

hpon

daq

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ctfo

otba

ll_fie

ldha

yfie

ldaq

uariu

mde

sert/

vege

tatio

nap

artm

ent_

build

ing/

outd

oor

form

al_g

arde

nfo

unta

inga

rage

/indo

oram

usem

ent_

park

gaze

bo/e

xter

ior

amus

emen

t_ar

cade

gene

ral_s

tore

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door

gift_

shop

glac

ier

golf_

cour

segr

otto

gym

nasiu

m/in

door

hang

ar/o

utdo

orfie

ld_r

oad

field

/cul

tivat

edfle

a_m

arke

t/ind

oor

fast

food

_res

taur

ant

art_

stud

iodo

wnto

wnar

t_ga

llery

arm

y_ba

sedr

ivew

aydr

ugst

ore

hote

l/out

door

hot_

sprin

gen

gine

_roo

mha

rdwa

re_s

tore

hosp

ital

exca

vatio

nar

cade

plaz

ask

yco

rrido

rsw

imm

ing_

pool

/indo

ortre

e_ho

use

door

way/

outd

oor

junk

yard

bank

_vau

ltlo

cker

_roo

mda

mla

ndfil

lch

urch

/indo

orba

zaar

/indo

orde

sert_

road

arch

ive

kitc

hen

alle

yice

berg

iglo

ooi

lrig

fire_

stat

ion

moa

t/wat

erpe

t_sh

opm

ovie

_the

ater

/indo

ordo

rm_r

oom

chal

etba

sem

ent

socc

er_f

ield

arch

aelo

gica

l_exc

avat

ion

harb

oral

cove

aren

a/ho

ckey

utilit

y_ro

omai

rfiel

dop

erat

ing_

room

cros

swal

kbo

atho

use

gree

nhou

se/in

door

laun

drom

atge

nera

l_sto

re/in

door

past

ure

ruin

dres

sing_

room

cana

l/urb

anpr

omen

ade

dese

rt/sa

ndpu

b/in

door

boar

dwal

kba

kery

/sho

pice

_cre

am_p

arlo

rar

chte

levi

sion_

stud

iosk

i_slo

pefa

bric_

stor

efo

rest

/bro

adle

afnu

rser

ylib

rary

/out

door

dine

r/out

door

airp

lane

_cab

inen

tranc

e_ha

llpa

rkin

g_lo

tre

crea

tion_

room

bow_

wind

ow/in

door

ball_

pit

slum

picn

ic_ar

eato

piar

y_ga

rden

attic

fore

st_r

oad

child

s_ro

ombu

s_in

terio

rpa

vilio

nsh

oe_s

hop

bath

room

mus

eum

/out

door

amph

ithea

ter

esca

lato

r/ind

oor

floris

t_sh

op/in

door

boat

_dec

k

-50%

-40%

-30%

-20%

-10%

0%

10%

Clas

s Acc

urac

y Dr

op

overall accuracy: 50.6%overall accuracy after ablating unit 116: 50.3%

windmillheliportwind_farmstage/indoororchestra_pit

ambu

lanc

eto

w tru

ck, t

ow c

ar, w

reck

erpo

p bo

ttle,

soda

bot

tleba

rber

shop

butc

her s

hop,

mea

t mar

ket

stre

tche

rho

rse

cart,

hor

se-c

art

padd

lewh

eel,

padd

le w

heel

fireb

oat

cinem

a, m

ovie

thea

ter,

mov

ie th

eatre

, mov

ie h

ouse

, pict

ure

pala

ceha

ndke

rchi

ef, h

anki

e, h

anky

, han

key

toba

cco

shop

, tob

acco

nist

shop

, tob

acco

nist

gown

mai

llot

cont

aine

r shi

p, c

onta

iner

ship

, con

tain

er v

esse

lfir

e en

gine

, fire

truc

kCh

ristm

as st

ocki

ng safe

jers

ey, T

-shi

rt, te

e sh

irtto

ucan

buck

et, p

ail

trim

aran

kim

ono

mea

surin

g cu

pre

d wi

neen

velo

pekn

otsh

oppi

ng b

aske

tfo

otba

ll he

lmet sk

ipu

ck, h

ocke

y pu

ckEs

kim

o do

g, h

usky bib

trom

bone

recr

eatio

nal v

ehicl

e, R

V, R

.V.

rack

et, r

acqu

etbo

bsle

d, b

obsle

igh,

bob

stop

watc

h, st

op w

atch

rugb

y ba

llre

frige

rato

r, ice

box

base

ball

pug,

pug

-dog

Bord

er c

ollie

mix

ing

bowl

sock

para

chut

e, c

hute

hotd

og, h

ot d

og, r

ed h

otSh

etla

nd sh

eepd

og, S

hetla

nd sh

eep

dog,

She

tland

pray

er ru

g, p

raye

r mat

spee

dboa

tlip

stick

, lip

roug

epo

tpie

jinrik

isha,

rick

sha,

rick

shaw

tree

frog,

tree

-frog

baro

met

erra

dio,

wire

less

pole

cat,

fitch

, fou

lmar

t, fo

umar

t, M

uste

la p

utor

ius

cash

mac

hine

, cas

h di

spen

ser,

auto

mat

ed te

ller m

achi

ne, a

utom

atic

telle

r mac

hine

, aut

omat

ed te

ller,

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ulpe

s vul

pes

Arct

ic fo

x, w

hite

fox,

Alo

pex

lago

pus

grey

fox,

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y fo

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rocy

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cat

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oach

dog

, car

riage

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Fren

ch b

ulld

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beta

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astif

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garia

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inte

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tter,

red

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Engl

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ringe

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glish

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pani

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span

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Suss

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ater

span

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kuva

szsc

hipp

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groe

nend

ael

pelic

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alin

ois

kom

ondo

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llieBo

uvie

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Fla

ndre

s, Bo

uvie

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es F

land

res

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eile

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rman

shep

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, Ger

man

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dog

, Ger

man

pol

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og, a

lsatia

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berm

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ober

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pin

sche

rm

inia

ture

pin

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eate

r Swi

ss M

ount

ain

dog

Bern

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mou

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penz

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tleBu

cher

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atch

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witc

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rudd

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Are

naria

inte

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box

torto

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erica

n ch

amel

eon,

ano

le, A

nolis

car

olin

ensis

whip

tail,

whi

ptai

l liza

rdag

ama

frille

d liz

ard,

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amyd

osau

rus k

ingi

allig

ator

liza

rdGi

la m

onst

er, H

elod

erm

a su

spec

tum

gree

n liz

ard,

Lac

erta

viri

dis

Afric

an c

ham

eleo

n, C

ham

aele

o ch

amae

leon

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odo

drag

on, K

omod

o liz

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dra

gon

lizar

d, g

iant

liza

rd, V

aran

us k

omod

oens

isAf

rican

cro

codi

le, N

ile c

roco

dile

, Cro

cody

lus n

ilotic

usAm

erica

n al

ligat

or, A

lligat

or m

ississ

ipie

nsis

trice

rato

psth

unde

r sna

ke, w

orm

snak

e, C

arph

ophi

s am

oenu

srin

gnec

k sn

ake,

ring

-nec

ked

snak

e, ri

ng sn

ake

gree

n sn

ake,

gra

ss sn

ake

garte

r sna

ke, g

rass

snak

ewa

ter s

nake

vine

snak

eni

ght s

nake

, Hyp

sigle

na to

rqua

tabo

a co

nstri

ctor

, Con

stric

tor c

onst

ricto

rro

ck p

ytho

n, ro

ck sn

ake,

Pyt

hon

seba

eIn

dian

cob

ra, N

aja

naja

terra

pin

mud

turtl

ele

athe

rbac

k tu

rtle,

leat

herb

ack,

leat

hery

turtl

e, D

erm

oche

lys c

oria

cea

logg

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ad, l

ogge

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d tu

rtle,

Car

etta

car

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grea

t whi

te sh

ark,

whi

te sh

ark,

man

-eat

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an-e

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g sh

ark,

Car

char

odon

car

char

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ham

mer

head

, ham

mer

head

shar

kel

ectri

c ra

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ram

pfish

, num

bfish

, tor

pedo

stin

gray hen

ostri

ch, S

truth

io c

amel

usbr

ambl

ing,

Frin

gilla

mon

tifrin

gilla

gold

finch

, Car

duel

is ca

rdue

lisho

use

finch

, lin

net,

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odac

us m

exica

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indi

go b

untin

g, in

digo

finc

h, in

digo

bird

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serin

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anea

bulb

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agle

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erica

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gle,

Hal

iaee

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euco

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regr

eat g

rey

owl,

grea

t gra

y ow

l, St

rix n

ebul

osa

Euro

pean

fire

sala

man

der,

Sala

man

dra

sala

man

dra

com

mon

new

t, Tr

ituru

s vul

garis

spot

ted

sala

man

der,

Amby

stom

a m

acul

atum

bullf

rog,

Ran

a ca

tesb

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ata

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frog,

bel

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bbed

toad

, tai

led

toad

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aphu

s tru

im

agpi

etig

er c

atse

a sn

ake

diam

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ack,

dia

mon

dbac

k ra

ttles

nake

, Cro

talu

s ada

man

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koal

a, k

oala

bea

r, ka

ngar

oo b

ear,

nativ

e be

ar, P

hasc

olar

ctos

cin

ereu

swo

mba

tje

llyfis

hse

a an

emon

e, a

nem

one

brai

n co

ral

nem

atod

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emat

ode

worm

, rou

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nch

snai

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gch

iton,

coa

t-of-m

ail s

hell,

sea

crad

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olyp

laco

phor

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rab,

Can

cer i

rrora

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fiddl

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rab

Amer

ican

lobs

ter,

North

ern

lobs

ter,

Mai

ne lo

bste

r, Ho

mar

us a

mer

icanu

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iny

lobs

ter,

lang

oust

e, ro

ck lo

bste

r, cr

awfis

h, c

rayf

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ea c

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crab

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stor

k, C

iconi

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gra

flam

ingo

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ican

egre

t, gr

eat w

hite

her

on, E

gret

ta a

lbus

limpk

in, A

ram

us p

ictus

Euro

pean

gal

linul

e, P

orph

yrio

por

phyr

ioAm

erica

n co

ot, m

arsh

hen

, mud

hen

, wat

er h

en, F

ulica

am

erica

nabu

star

dwa

llaby

, bru

sh k

anga

roo

plat

ypus

, duc

kbill,

duc

kbille

d pl

atyp

us, d

uck-

bille

d pl

atyp

us, O

rnith

orhy

nchu

s ana

tinus

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dna,

spin

y an

teat

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ntea

ter

tusk

ersid

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der,

horn

ed ra

ttles

nake

, Cro

talu

s cer

aste

stri

lobi

teha

rves

tman

, dad

dy lo

ngle

gs, P

hala

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m o

pilio

scor

pion

blac

k an

d go

ld g

arde

n sp

ider

, Arg

iope

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antia

gard

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nea

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ack

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atro

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wolf

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cent

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eras

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sand

vip

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ouse

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fowl

peac

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coc

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horn

bill

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ane

prob

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us d

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ink

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foot

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ferre

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uste

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anist

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alus

trade

, bal

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andr

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banj

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pol

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badg

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mad

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nt, b

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pen

, bal

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lawn

car

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car,

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, sta

tion

wagg

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ng tu

b, b

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towe

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ing

cap,

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min

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bra

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wild

boa

r, bo

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us sc

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warth

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popo

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ter b

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ater

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ghor

n, b

igho

rn sh

eep,

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arro

n, R

ocky

Mou

ntai

n bi

ghor

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ocky

Mou

ntai

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eep,

Ovi

s can

aden

sisth

ree-

toed

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h, a

i, Br

adyp

us tr

idac

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l, ea

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n fo

x sq

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l, Sc

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s nig

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tan,

ora

ng, o

rang

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g, P

ongo

pyg

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, Gor

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hip,

diri

gibl

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rline

rIn

dian

ele

phan

t, El

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s max

imus

Afric

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Loxo

dont

a af

rican

agi

ant p

anda

, pan

da, p

anda

bea

r, co

on b

ear,

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da m

elan

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ca eel

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, coh

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oho

salm

on, b

lue

jack

, silv

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lmon

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s kisu

tch

indr

i, in

dris,

Indr

i ind

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revi

caud

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cord

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pia

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han

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e fis

hlio

nfish

rock

bea

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Hol

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thus

trico

lor

Mad

agas

car c

at, r

ing-

taile

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mur

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ur c

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spid

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onke

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tele

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panz

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him

p, P

an tr

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kery

, bak

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p, b

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gibb

on, H

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lar

back

pack

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k pa

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naps

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pac

ksac

k, ru

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aver

sack

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ang,

Hyl

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s, Sy

mph

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gus s

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, gue

non

mon

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s, hu

ssar

mon

key,

Ery

thro

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s pat

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boon

mac

aque

assa

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ass

ault

gun

lang

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, tra

sh c

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bin,

ash

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, ash

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, ash

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, tra

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arre

l, tra

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inco

lobu

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key

mar

mos

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in, r

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Cebu

s cap

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usan

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clo

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wler

mon

key,

how

ler

bala

nce

beam

, bea

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rcup

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stur

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leaf

bee

tle, c

hrys

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, tap

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ax li

ght

ham

ster

snow

leop

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oun

ce, P

anth

era

uncia

dung

bee

tlelio

n, k

ing

of b

east

s, Pa

nthe

ra le

orh

inoc

eros

bee

tle fly bee

ant,

emm

et, p

ismire

crick

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nnon

book

case

weev

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noe

long

-hor

ned

beet

le, l

ongi

corn

, lon

gico

rn b

eetle

bras

s, m

emor

ial t

able

t, pl

aque

buck

lebr

own

bear

, bru

in, U

rsus

arc

tos

brea

stpl

ate,

aeg

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gis

Amer

ican

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k be

ar, b

lack

bea

r, Ur

sus a

mer

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s, Eu

arct

os a

mer

icanu

sbu

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rain

, bul

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brea

kwat

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roin

, gro

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mol

e, b

ulwa

rk, s

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ttygr

ound

bee

tle, c

arab

id b

eetle

tiger

, Pan

ther

a tig

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th b

ear,

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s urs

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, Urs

us u

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usm

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mee

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, mie

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bras

siere

, bra

, ban

deau

tiger

bee

tlela

dybu

g, la

dybe

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, lad

y be

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, lad

ybird

, lad

ybird

bee

tleice

bea

r, po

lar b

ear,

Ursu

s Mar

itim

us, T

hala

rcto

s mar

itim

usch

eeta

h, c

heta

h, A

cinon

yx ju

batu

sco

ckro

ach,

roac

hbu

lletp

roof

ves

two

od ra

bbit,

cot

tont

ail,

cotto

ntai

l rab

bit

sea

cucu

mbe

r, ho

loth

uria

nSi

ames

e ca

t, Si

ames

eco

ugar

, pum

a, c

atam

ount

, mou

ntai

n lio

n, p

aint

er, p

anth

er, F

elis

conc

olor

sea

urch

inly

nx, c

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beac

on, l

ight

hous

e, b

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n lig

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a st

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ottle

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, lyc

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d bu

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ll co

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lfur b

utte

rfly

bind

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ing-

bind

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antis

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e bu

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, fie

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lass

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bol

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Pan

ther

a pa

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cicad

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can

open

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usby

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flyda

mse

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Ango

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bird

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ead

mira

lrin

glet

, rin

glet

but

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onar

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utte

rfly,

milk

weed

but

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anau

s ple

xipp

usdr

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fly, d

arni

ng n

eedl

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evil's

dar

ning

nee

dle,

sewi

ng n

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feed

er, s

nake

doc

tor,

mos

quito

haw

k, sk

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r haw

kca

rous

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arro

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, mer

ry-g

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roun

dabo

ut, w

hirli

gig

pack

etch

est

Dung

enes

s cra

b, C

ance

r mag

ister

warp

lane

, milit

ary

plan

est

udio

cou

ch, d

ay b

edun

icycle

, mon

ocyc

lewa

lkin

g st

ick, w

alki

ngst

ick, s

tick

inse

ctpa

npip

e, p

ande

an p

ipe,

syrin

xse

at b

elt,

seat

belt

Bost

on b

ull,

Bost

on te

rrier

Wel

sh sp

ringe

r spa

niel

card

igan

kelp

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mm

on ig

uana

, igu

ana,

Igua

na ig

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fan,

blo

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dom

ere

d-br

east

ed m

erga

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gus s

erra

tor

cros

swor

d pu

zzle

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sswo

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enhe

im sp

anie

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senj

ipl

ane,

car

pent

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plan

e, w

oodw

orki

ng p

lane

alta

ram

phib

ian,

am

phib

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veh

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blac

k sw

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s atra

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poo

dle

burri

toco

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iral,

volu

te, w

horl,

hel

ixcu

cum

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cuke

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wmill

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tank

suit

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mas

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, pan

ther

, Pan

ther

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ca, F

elis

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alba

tross

, mol

lym

awk

affe

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sche

r, m

onke

y pi

nsch

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onke

y do

gwo

rm fe

nce,

snak

e fe

nce,

snak

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il fe

nce,

Virg

inia

fenc

em

arm

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spen

sion

brid

gesh

oe sh

op, s

hoe-

shop

, sho

e st

ore

buck

eye,

hor

se c

hest

nut,

conk

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rain

erre

d wo

lf, m

aned

wol

f, Ca

nis r

ufus

, Can

is ni

ger

Old

Engl

ish sh

eepd

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obta

ilva

cuum

, vac

uum

cle

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plun

ger,

plum

ber's

hel

per

chur

ch, c

hurc

h bu

ildin

gdi

gita

l clo

ckm

inia

ture

poo

dle

spac

e ba

rro

bin,

Am

erica

n ro

bin,

Tur

dus m

igra

toriu

sbi

ttern

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esso

conv

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a, p

j's, j

amm

ies

reel

kit f

ox, V

ulpe

s mac

rotis

patio

, ter

race

oboe

, hau

tboy

, hau

tboi

sm

onito

rco

rnet

, hor

n, tr

umpe

t, tru

mp

ladl

esu

ngla

sses

, dar

k gl

asse

s, sh

ades

gar,

garfi

sh, g

arpi

ke, b

illfish

, Lep

isost

eus o

sseu

sum

brel

lafo

ldin

g ch

air

chiff

onie

r, co

mm

ode

desk

top

com

pute

rCD

pla

yer

cran

epe

ncil

shar

pene

rtri

pod

rubb

er e

rase

r, ru

bber

, pen

cil e

rase

rcr

ayfis

h, c

rawf

ish, c

rawd

ad, c

rawd

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pitc

her,

ewer

shov

elch

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ate

sauc

e, c

hoco

late

syru

pda

m, d

ike,

dyk

eho

gnos

e sn

ake,

puf

f add

er, s

and

vipe

rsk

i mas

kgo

lden

retri

ever

com

pute

r key

boar

d, k

eypa

dru

le, r

uler

gas p

ump,

gas

olin

e pu

mp,

pet

rol p

ump,

isla

nd d

ispen

ser

face

pow

der

flatw

orm

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tyhe

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thtu

rnst

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us, t

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y co

ach,

trac

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s tro

lley

tiger

shar

k, G

aleo

cerd

o cu

vier

iwa

ll clo

ckki

ng c

rab,

Ala

ska

crab

, Ala

skan

kin

g cr

ab, A

lask

a ki

ng c

rab,

Par

alith

odes

cam

tsch

atica

whisk

ey ju

gpt

arm

igan

Mod

el T

stov

ere

dbon

eod

omet

er, h

odom

eter

, mile

omet

er, m

ilom

eter

cab,

hac

k, ta

xi, t

axica

bgu

acam

ole

junc

o, sn

owbi

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urgl

ass

com

ic bo

okca

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little

blu

e he

ron,

Egr

etta

cae

rule

ala

wn m

ower

, mow

ergo

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llha

rpm

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ag, p

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car

, spo

rt ca

rco

ckta

il sh

aker

pay-

phon

e, p

ay-s

tatio

npa

lace

airc

raft

carri

er, c

arrie

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ck a

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ft ca

rrier

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hank

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ga to

tanu

stra

ffic

light

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ffic

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oplig

htwh

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oque

t bal

lst

eel d

rum

mas

hed

pota

todi

aper

, nap

py, n

apki

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orn

squa

shfu

r coa

tm

edici

ne c

hest

, med

icine

cab

inet

lotio

nbo

oksh

op, b

ooks

tore

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ksta

llgo

lfcar

t, go

lf ca

rtpo

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van,

pol

ice w

agon

, pad

dy w

agon

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rol w

agon

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on, b

lack

Mar

iabe

agle

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ron,

cau

ldro

nto

y te

rrier

Engl

ish se

tter

mob

ile h

ome,

man

ufac

ture

d ho

me

libra

rych

ain

saw,

cha

insa

wice

cre

am, i

cecr

eam

barra

cout

a, sn

oek

toys

hop

guillo

tine

conf

ectio

nery

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fect

iona

ry, c

andy

stor

epi

ckup

, pick

up tr

uck

cowb

oy h

at, t

en-g

allo

n ha

tdr

illing

pla

tform

, offs

hore

rig

fork

lift

apia

ry, b

ee h

ouse

apro

nje

ep, l

andr

over

min

ibus

-50%

-40%

-30%

-20%

-10%

0%

10%

Clas

s Acc

urac

y Dr

op

overall accuracy: 55.6%overall accuracy after ablating unit 83: 55.5%

ambulancetow truck, tow car, wreckerpop bottle, soda bottlebarbershopbutcher shop, meat market

spag

hetti

squa

shle

mon

acor

n sq

uash

bana

nago

ldfin

ch, C

ardu

elis

card

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butte

rnut

squa

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r, sp

ike,

cap

itulu

msu

lphu

r but

terfl

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lfur b

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hini

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rget

tech

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burg

ersq

uirre

l mon

key,

Sai

miri

sciu

reus

jack

fruit,

jak,

jack

mea

t loa

f, m

eatlo

afro

ck b

eaut

y, H

oloc

anth

us tr

icolo

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inoc

eros

bee

tlebo

x tu

rtle,

box

torto

iseye

llow

lady

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pper

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low

lady

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per,

Cypr

iped

ium

cal

ceol

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yprip

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m p

arvi

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un b

lock

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ter j

ugwi

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ottle

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acuu

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mm

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rope

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lam

ande

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lam

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lam

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nowp

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acer

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iridi

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bm

ount

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sea

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ane

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edo

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horn

bill

wash

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utom

atic

wash

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ashi

ng m

achi

nepa

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ande

an p

ipe,

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e flyba

sket

ball

dish

rag,

dish

cloth

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met

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b, h

ack,

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icab

spee

dboa

thi

p, ro

se h

ip, r

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ial

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e ea

ter

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ask

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den

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eelb

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ink

trifle

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rwich

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bus

Ches

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ay re

triev

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wmill

Shih

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buck

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ail

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spid

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rane

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adem

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ic bo

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ow d

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hair

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ir dr

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shop

ping

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ket

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g pa

n, fr

ypan

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llet

gown

Fren

ch h

orn,

hor

nIta

lian

grey

houn

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ler's

loup

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eat D

ane

flute

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nsve

rse

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il sh

arpe

ner

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get

a, p

atte

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bot

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r dril

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utte

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int B

erna

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t Ber

nard

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ican

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eat w

hite

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gret

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lbus

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ael

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erto

ucan

mac

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ram

pfish

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pedo

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ueno

n m

onke

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pay

-sta

tion

conv

ertib

leca

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ara

pine

appl

e, a

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pper

Indi

an c

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a na

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ggy

bank

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ny b

ank

mal

inoi

spi

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elm

ouse

trap

plas

tic b

agsc

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river

pack

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jam

a, p

yjam

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ies

whist

leco

nfec

tione

ry, c

onfe

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nary

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ore

cowb

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at, t

en-g

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n ha

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retc

her

choc

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e sa

uce,

cho

cola

te sy

rup

curly

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ted

retri

ever

grou

nd b

eetle

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abid

bee

tlebu

ll m

astif

fm

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etze

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lawn

mow

er, m

ower

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eile

ras

hcan

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sh c

an, g

arba

ge c

an, w

aste

bin,

ash

bin

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-bin

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sh b

arre

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inpl

ate

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d gr

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tridg

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onas

a um

bellu

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inea

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ia c

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abl

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iwe

evil

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auze

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odes

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padd

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oat p

addl

em

ovin

g va

ngr

een

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baca

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up b

owl

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pm

ount

ain

bike

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terra

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ike,

off-

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uzzle na

illo

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udsp

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aker

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afer

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bra

cene

ckla

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pple

note

book

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eboo

k co

mpu

ter

oboe

, hau

tboy

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slip

stick

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roug

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cean

line

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ousin

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mo

oil f

ilter

light

er, l

ight

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iter,

igni

tor

orga

n, p

ipe

orga

nos

cillo

scop

e, sc

ope,

cat

hode

-ray

oscil

losc

ope,

CRO

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over

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otor

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em

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enm

ilk c

anm

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ini

min

ivan

miss

ilem

atch

stick

mitt

enm

ask

mos

quito

net

mix

ing

bowl

mar

imba

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over

mod

emm

aillo

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nk su

itm

onas

tery

mon

itor

mop

edm

ailb

ox, l

ette

r box

mai

lbag

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tbag

mor

tar

mor

tarb

oard

Mod

el T

libra

ryte

nch,

Tin

ca ti

nca

lens

cap

, len

s cov

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aper

, nap

py, n

apki

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gita

l clo

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ning

tabl

e, b

oard

disk

bra

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rake

dock

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kage

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facil

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d, d

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enve

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aker

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pute

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r kni

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ball

helm

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stile

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ight

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nova

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ult

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lake

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Win

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ear,

Mel

ursu

s urs

inus

, Urs

us u

rsin

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sh m

achi

ne, c

ash

disp

ense

r, au

tom

ated

telle

r mac

hine

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omat

ic te

ller m

achi

ne, a

utom

ated

telle

r, au

tom

atic

telle

r, AT

Mm

ongo

ose

mee

rkat

, mie

rkat

tiger

bee

tlela

dybu

g, la

dybe

etle

, lad

y be

etle

, lad

ybird

, lad

ybird

bee

tlelo

ng-h

orne

d be

etle

, lon

gico

rn, l

ongi

corn

bee

tledu

ng b

eetle

ant,

emm

et, p

ismire

wate

r buf

falo

, wat

er o

x, A

siatic

buf

falo

, Bub

alus

bub

alis

carto

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rpen

ter's

kit,

tool

kit

caro

usel

, car

rous

el, m

erry

-go-

roun

d, ro

unda

bout

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rligi

gcr

icket

walk

ing

stick

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king

stick

, stic

k in

sect

ice b

ear,

pola

r bea

r, Ur

sus M

ariti

mus

, Tha

larc

tos m

ariti

mus

Amer

ican

blac

k be

ar, b

lack

bea

r, Ur

sus a

mer

icanu

s, Eu

arct

os a

mer

icanu

sbr

own

bear

, bru

in, U

rsus

arc

tos

CD p

laye

rAr

ctic

fox,

whi

te fo

x, A

lope

x la

gopu

sgr

ey fo

x, g

ray

fox,

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cyon

cin

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er c

atch

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ink

fenc

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n ca

tSi

ames

e ca

t, Si

ames

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r mirr

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a, c

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, mou

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n, p

aint

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anth

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elis

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olor

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d, P

anth

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pard

usce

llula

r tel

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lar p

hone

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lpho

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ell,

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ile p

hone

snow

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oun

ce, P

anth

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uncia

cello

, vio

lonc

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lion,

kin

g of

bea

sts,

Pant

hera

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tiger

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ther

a tig

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h, c

heta

h, A

cinon

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batu

sch

ain

man

tis, m

antid

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heel

sea

cucu

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loth

uria

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star

fish,

sea

star

sea

urch

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r sho

p, m

eat m

arke

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aver

wood

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otto

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ttont

ail r

abbi

tm

arm

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ster

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x sq

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l, Sc

iuru

s nig

erca

rdig

anca

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, tap

er, w

ax li

ght

bulle

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of v

est

porc

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edge

hog

bulle

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in, b

ulle

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noe

mon

arch

, mon

arch

but

terfl

y, m

ilkwe

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utte

rfly,

Dan

aus p

lexi

ppus

hipp

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amus

, hip

po, r

iver

hor

se, H

ippo

pota

mus

am

phib

ius

leaf

hopp

erca

n op

ener

, tin

ope

ner

warth

ogwi

ld b

oar,

boar

, Sus

scro

fadr

agon

fly, d

arni

ng n

eedl

e, d

evil's

dar

ning

nee

dle,

sewi

ng n

eedl

e, sn

ake

feed

er, s

nake

doc

tor,

mos

quito

haw

k, sk

eete

r haw

kda

mse

lfly

Ango

ra, A

ngor

a ra

bbit

buck

lead

mira

lho

g, p

ig, g

runt

er, s

quea

ler,

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crof

abr

east

plat

e, a

egis,

egi

sca

ldro

n, c

auld

ron

ringn

eck

snak

e, ri

ng-n

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d sn

ake,

ring

snak

est

rawb

erry

min

ibus

Gran

ny S

mith

trim

aran

stin

khor

n, c

arrio

n fu

ngus

vend

ing

mac

hine

tract

orgr

eenh

ouse

, nur

sery

, gla

ssho

use

spac

e sh

uttle

ambu

lanc

ebi

rdho

use

peac

ock

warp

lane

, milit

ary

plan

eha

rein

dri,

indr

is, In

dri i

ndri,

Indr

i bre

vica

udat

usth

ree-

toed

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h, a

i, Br

adyp

us tr

idac

tylu

sgr

ocer

y st

ore,

gro

cery

, foo

d m

arke

t, m

arke

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r cur

tain

, the

atre

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hip,

diri

gibl

ele

af b

eetle

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ysom

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card

oon

wate

r sna

kera

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wire

less

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quee

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oxba

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inia

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ctor

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stric

tor c

onst

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amon

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k, d

iam

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ack

rattl

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ke, C

rota

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dam

ante

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tdog

, hot

dog

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hot

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bbel

lki

t fox

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pes m

acro

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ambe

red

naut

ilus,

pear

ly n

autil

us, n

autil

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ho, c

ohoe

, coh

o sa

lmon

, blu

e ja

ck, s

ilver

salm

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ncor

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outh

org

an, h

arp,

mou

th h

arp

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rsh

oe sh

op, s

hoe-

shop

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e st

ore

scor

eboa

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y te

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med

icine

che

st, m

edici

ne c

abin

etsu

spen

sion

brid

geca

bbag

e bu

tterfl

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uta,

snoe

kro

ck c

rab,

Can

cer i

rrora

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cicad

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cock

roac

h, ro

ach

slide

rule

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stick

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h, e

lect

ric sw

itch,

ele

ctric

al sw

itch

lotio

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moy

ed, S

amoy

ede

sea

slug,

nud

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nch

stra

iner

spid

er w

eb, s

pide

r's w

ebwa

llaby

, bru

sh k

anga

roo

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age

truck

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ound

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asca

r cat

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g-ta

iled

lem

ur, L

emur

cat

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, pan

ther

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ther

a on

ca, F

elis

onca

four

-pos

ter

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iddl

eki

mon

om

ouse

, com

pute

r mou

sede

skto

p co

mpu

ter

bow

hair

slide

cray

fish,

cra

wfish

, cra

wdad

, cra

wdad

dyvi

ne sn

ake

tray

mus

hroo

mta

pe p

laye

rpi

zza,

pizz

a pi

epo

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milit

ary

unifo

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obile

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e, m

anuf

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red

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egr

assh

oppe

r, ho

pper

Arab

ian

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el, d

rom

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amel

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rom

edar

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otat

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lift

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hair

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tal w

atch

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lask

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lask

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ing

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ska

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aral

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stov

edi

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sher

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her,

dish

wash

ing

mac

hine

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mig

an yurt

mas

hed

pota

tofla

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m, p

laty

helm

inth

tow

truck

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car

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ream

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crea

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cons

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anist

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alus

trade

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uste

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andr

ail

chai

n sa

w, c

hain

saw

kite

Croc

k Po

tm

inia

ture

pin

sche

rro

tisse

riem

arm

oset

gong

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-tam

couc

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and

gold

gar

den

spid

er, A

rgio

pe a

uran

tiasa

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r, sa

nd b

arru

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ball

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up tr

uck

cran

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lley

coac

h, tr

ackl

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rolle

ybo

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cket

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t cov

er, d

ust j

acke

t, du

st w

rapp

erco

il, sp

iral,

volu

te, w

horl,

hel

ixga

r, ga

rfish

, gar

pike

, billf

ish, L

episo

steu

s oss

eus

Engl

ish se

tter

Amer

ican

lobs

ter,

North

ern

lobs

ter,

Mai

ne lo

bste

r, Ho

mar

us a

mer

icanu

sAm

erica

n ch

amel

eon,

ano

le, A

nolis

car

olin

ensis

caul

iflow

erha

rtebe

est

corn

Dung

enes

s cra

b, C

ance

r mag

ister

lace

wing

, lac

ewin

g fly

bullf

rog,

Ran

a ca

tesb

eian

aFr

ench

loaf

cucu

mbe

r, cu

keor

ange

anem

one

fish

Band

Aid

pata

s, hu

ssar

mon

key,

Ery

thro

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s pat

ases

pres

so

-50%

-40%

-30%

-20%

-10%

0%

10%

Clas

s Acc

urac

y Dr

op

overall accuracy: 55.6%overall accuracy after ablating unit 78: 55.5%

spaghetti squashlemonacorn squashbananagoldfinch, Carduelis carduelis

Mod

el T

bicy

cle-b

uilt-

for-t

wo, t

ande

m b

icycle

, tan

dem

unicy

cle, m

onoc

ycle

jinrik

isha,

rick

sha,

rick

shaw

mop

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ain

bike

, all-

terra

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off-

road

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hras

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shin

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achi

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car w

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plow

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buck

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lam

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felim

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half

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Fren

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tbl

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and

gold

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cim

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Mou

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Ovi

s can

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dive

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fan,

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sand

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cont

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Sm

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se h

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Pode

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ance

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ip ro

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neck

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liner

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mou

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ht, i

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-50%

-40%

-30%

-20%

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0%

10%

Clas

s Acc

urac

y Dr

op

overall accuracy: 55.6%overall accuracy after ablating unit 197: 55.4%

Model Tbicycle-built-for-two, tandem bicycle, tandemunicycle, monocyclejinrikisha, ricksha, rickshawmoped

Figure 2: Ablated units from AlexNet-Places (top two units) and AlexNet-ImageNet (bottom threeunits) respectively. For the last unit in AlexNet-ImageNet which is selective to wheels, we alsoshow the image samples from the top three damaged classes in which wheels are important parts torepresent the classes. For each unit ablated, we show the unit visualization, the sorted class accuracydrops, and the overall accuracy before and after unit ablation. We can see that ablating one unit bringsa significant class accuracy drop to some specific classes, compared to the trivial drop in overallaccuracy. The most damaged classes match the unit visualization very well. A few classes also benefitslightly from the unit ablation, though they are not semantically related. We check the categorieswhich benefit from the unit ablation and they are not meaningful so that we don’t show them in detail.When averaging class accuracy drop to obtain the overall accuracy drop, the overall drop is small.

5

0 50 100 150 200 250 300

Sorted Unit IDX

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

Accura

cy D

rop

conv5

Overall Accuracy Drop

Max Class Accuracy Drop

Min Class Accuracy Drop

0 100 200 300 400

Sorted Unit IDX

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Accura

cy D

rop

conv3

Overall Accuracy Drop

Max Class Accuracy Drop

Min Class Accuracy Drop

0 20 40 60 80 100

Sorted Unit IDX

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Accura

cy D

rop

conv1

Overall Accuracy Drop

Max Class Accuracy Drop

Min Class Accuracy Drop

Figure 3: Units at each one of the three layers from AlexNet-Places are sorted by their max classaccuracy drops. For each unit we also plot the min class accuracy drop and the overall accuracy drop.Ablating units has different effect on overall accuracy and class accuracy.

accuracy drop. We can see that some classes are damaged significantly when ablating a single unit.For example, ablating a unit which is sensitive to waterfalls (as seen in the visualization) hurts thecategories waterfall, fountain, hot spring. Specifically, the category of waterfall suffers a huge 50%drop in class accuracy. In the third example, ablating a unit that is sensitive to trade signs on a whitebackground impacts the categories of ambulance, tow truck, and pop bottle significantly.

For all the units at one layer, we take the max class accuracy drop as well as the min class accuracydrop for each unit then sort all the units by their max class accuracy. Note that the min class accuracydrop reveals a slight gain in accuracy for some classes when a unit is ablated. This provides anoverview of each unit’s importance to individual classes. The result on the three convolutional layersof AlexNet-Places is plotted in Fig. 3. The drop in overall accuracy (blue curve) is very small (below1%), which is in agreement with results shown in [11]. Regardless of the characteristics of a unit,the effect of a single unit ablation on overall accuracy is marginal. However, observing max classaccuracy drop (red curve) reveals a different picture. The drop in performance for individual classescan be very significant. For instance, in the conv5 layer, the max drop is 50%, while more than halfof the units produce a drop above 10% on individual classes if ablated. As shown previously in Fig.1removing a ‘bed’ selective unit strongly affects the recognition of bedrooms and hotel rooms. Theoverall impact on the classification accuracy for the other categories is negligible, but a networkmissing that unit will have a higher error rate when tested on bedrooms. These results show thatindividual units tend to specialize: each unit plays an important role in recognizing a subset of specificclasses. Therefore, any analysis of unit importance should examine the effect on individual classes; ananalysis based only on overall performance can miss important effects when manipulating a network.

Greedy Unit Ablation. To further explore how groups of units collectively contribute to predictionsfor a class, we conduct an experiment in which we ablate a series of units successively. In thisexperiment, we follow a greedy approach, iteratively ablating additional units that maximally reducethe accuracy of a specific class.

The result of greedy unit ablation on conv5 layer of AlexNet-Places is shown in Fig.4. Fig.4a showsthat class accuracy drops sharply when the number of units ablated increases, with accuracy for aspecific class dropping to very low levels after 10-15 units are ablated. In Fig.4b, we further plotclass accuracy drop over the number of units ablated averaged across all the categories (for each classwe conduct a separate series of greedy unit ablations). This is compared with the effect of ablationsof a randomly chosen sequence of units as a baseline comparison.

We find that ablating top 5 most informative units for a class brings about 28% class accuracy drop,and top 10 most informative units units for more than 40% class accuracy drop. On the contrary thereis negligible change in class accuracy if a random set of units is ablated.

3.3 Relation between the Accuracy Drop and other Attributes

What attributes does a unit have that make it important to class-specific and overall generalizationaccuracy? For insight, we analyze the relation between a set of unit attributes and the ablationaccuracy drops. We examine unit class correlation and class selectivity in terms of the class withmaximum value; for concept IoU we examine the concept with maximum IoU value.

The results on AlexNet-Places365 are shown in Fig.5. We can see that for overall accuracy drop(the upper row), there are positive correlations for class selectivity, class correlation and concept

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pro enadeforest_road(egetable_gardensandbox arket/outdoorgazebo/exteriorbeach_househayfields)i ing_pool/indoorbank_(ault

Figure 4: a) The class accuracy over the number of units ablated for a random selection of classesfrom Places. b) The average max class accuracy drop over the number of units ablated in greedy unitablation.

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Figure 5: Overall accuracy drop (the upper row) and max accuracy drop (the lower row) over the unitattributes for all the units in AlexNet-Places. r is the Spearman’s rank-order correlation and p is thep-value. We also include a random value baseline in the first column.

alignment. That is, as these measures of alignment between a unit and a single concept decrease, theunit tends to cause a larger drop in overall accuracy when ablated. This finding is consistent withthe result in [11], where it was found that highly class selective units cause less damage to overallaccuracy when ablated. As argued in [11] this result questions the necessity of studying the unitswith high single direction selectivity as suggested in [20, 22]. Unit L1 stands out as the one metricthat correlates with overall accuracy drop.

On the other hand, examining the correlation between the unit attributes and max class accuracydrop shown at the lower row in Fig.5 shows a different story. There is a clear negative correlationbetween the max class accuracy drop and each of the measured attributes. Class selectivity and classcorrelation show particularly strong negative correlations: the more closely aligned a unit is witha class, the larger the max class accuracy drop. Alignment of a unit with a visual concept is alsocorrelated with max class accuracy drop, but not as strongly, indicating that concept-aligned unitsspread out their contribution to many classes. Interestingly we also find that L1 norm is a good metricfor unit importance in class accuracy as well. The utility of L1 to predict both overall and max classaccuracy impact may be a reason for the success of the L1 norm in network compression.

Predicting the class one unit contributes most using the attributes. From our previous analysis,we show that an individual unit is specialized to represent one or a subset of classes while theattributes are correlated with the max class accuracy drop. Here we further use the unit attributes topredict the class one individual unit contributes most, to see how predictable different metrics are.

We prepare five instances of AlexNet-Places trained with different random initialization. We usethe attributes of units from conv5 layer of three network instances for training; then we evaluate themodel on the units from conv5 layer of the other two network instances.

We test the use of three attributes Class Correlation, Class Selectivity, and Concept Alignment, asalternative features for training a simple logistic regression classifier. Note that we use the vector ofeach attributes rather than taking the maximum value as the features. The ground-truth label for eachunit is the index of the class which has the largest accuracy drop when that unit is ablated.

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Attribute Concept Alignment Class Correlation Class Selectivity EnsembleAccuracy 38.67% 38.28% 38.08% 39.06%

Table 1: Accuracy for predicting the class one unit contributes most using different attributes.Evaluated on the units of two held-out network instances, the attributes are reasonably well predictorsfor the unit importance to class. Here ensemble takes the average of the predicted probabilities fromthe previous three models.

Prediction accuracy using different attributes is shown in Table 1. We can see that Concept Alignmentoutperforms the other two attributes. This number exceeds the correlation measurement in Sec. 3.3because the model can adjust its sensitivity to aligned concepts according to their specific relevanceto classes in the target problem. The reasonably good prediction accuracy suggest that the units withboth high class selectivity and concept interpretability play an important role in recognizing specificclasses. Some prediction examples are shown in Fig.6.Unit 3, conv5 Groud Truth:

creek Top3 Prediction:

Unit 113, conv5 Groud Truth: dorm room

Top3 Prediction:

Groud Truth: auditorium

Top3 Prediction:auditorium

Unit 177, conv5

Figure 6: Examples of predicting the class one unit contributes most. The ground-truth class is fromthe class with max class accuracy drop when the unit is ablated. Top 3 predictions are given by theclassifier trained on Concept Alignment scores.

3.4 Unit Ablation Under Random Rotation

We have shown that some individual units are important for the generalization accuracy of specificclasses, which suggests the role of individual units is to specialize in information that is useful foronly a subset of decisions. To verify that this phenomenon is specific to unit directions and not merelya result of ablating random directions, we compare the measurements to a randomized baseline.

First, we obtain random directions in representation space by applying a random rotation on thelearned units, similar to the random projection of units in [1]. The random rotation of units createsa new set of random directions (the same as the number of units). Applying the inverse rotationrecovers the original representation, so classification accuracy is not affected. To ablate a randomdirection, we zero a coordinate in the rotated representation, erasing the selected random direction.

The max class accuracy drop curves are shown in first and third graphs in Fig. 7. It can be seen thatunits are more specialized towards individual classes than random rotated directions in representationspace, providing support to the hypothesis that generalization accuracy of individual classes does relyon specific units. The second and fourth graphs in Fig. 7 shows a similar comparison with respect tothe overall accuracy drop. Although again we find individual unit directions appear more importantthan arbitrary directions in representation space, notice that the scale is different: the comparativeimportance of individual units over random directions for overall generalization performance is small.

These comparisons reveal that not only are unit directions more important for generalization thanrandom directions in representation space, but that the contributions of most unit directions aresystematically focused on a subset of classes rather than generically on all classes.

3.5 Influence of Batch Normalization and Dropout on the Units

Batch normalization [7] and dropout [17] are effective training regularizers for deep neural networks.Batch normalization [7] speeds up the training process and enables the training of much deepernetworks [6], while dropout randomly blackout features in training to prevent overfitting [17].However, the impact of these two on the representation is not as well understood [21, 11]. Here wecompare the unit ablation on representation trained with batch normalization and with dropout. Thebaseline is the naive AlexNet-Places. To evaluate the batch norm, We add batch normalization on eachof the 5 conv layers on the baseline. The network with batch norm achieves similar generalizationaccuracy with the original baseline. To evaluate the dropout, we add channel dropout on conv5 layeronly (in each training batch 50% of channels/units are randomly dropped out; thus in experiment,

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layer4 at ResNet18 conv5 at AlexNet

Figure 7: Comparison of the max class accuracy drop curves for random directions in representationspace, on the conv5 layer of AlexNet and layer4 of ResNet18. The left image in each group:Sorted max class accuracy drop of unit ablations versus ablations of a basis of random directionsin representation space. The right image in each group: a similar comparison for overall accuracyacross all classes. Note that the scales of the second and fourth graph are amplified by 1000 times tomake the small gap visible.

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Figure 8: Comparison of the max class accuracy drop curves for the baseline, network with batchnormalization, and network with dropout. Since Batch norm is on all the conv layers, it reduces theclass-specific information carried by the individual units across all the layers. Dropout on conv5greatly reduces the max class accuracy drop for all the units.

dropout is applied to conv5, fc6, and fc7, wheras in baseline only fc6 and fc7 have dropout). Thenetwork with channel dropout achieved 2% lower accuracy. The max class accuracy drop curves areshown in Fig.8. Compared with the baseline, we can see that batch normalization and dropout reduceclass information contained in each individual unit. Interestingly dropout greatly reduces max classaccuracy drop for all the units, scattering class-specific information across all the units rather thanbeing concentrated. That reduces the alignment of individual units at conv5 with single interpretabledirections (see the network dissection result in Fig.9). However dropout on conv5 layer increases maxclass accuracy drop at lower conv4 layer as compensation. Thus batch normalization and dropout,initially proposed to address the issue of training efficiency and overfitting, also affect class-specificinformation carried by the units and their interpretability. Designing a regularizer that improves boththe classification performance and interpretability of individual units remains an open problem.

Baseline With Dropout2d

Figure 9: Histogram of interpretable units identified by network dissection [1] on the conv5 layer ofthe baseline and the network with channel dropout on conv5. Dropout greatly reduces the number ofinterpretable units in a layer.

4 Conclusion

By conducting a set of unit ablation experiments, we have refined our understanding of the importanceof individual units on the generalization accuracy of CNNs trained for visual recognition. We haveverified that removing one unit does not significantly damage overall network generalization accuracy,which is consistent with [11]. However, we have also found that removing an individual unit causessignificant damage to the accuracy of a subset of classes. The finding that individual units specializein subsets of the classification space indicates that the contribution of individual units towards thedecisions of a CNN is both nontrivial and specific. Understanding how individual units decompose anetwork’s predictions remains an important area for further study.

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Acknowledgement This work was partially funded by DARPA XAI program to A.T.;This work wasalso partly supported by the National Science Foundation under Grants No. 1524817 to A.T.. B.Z issupported by a Facebook Fellowship.

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