outline · outline 1. problem 2. teori ... “rich feature hierarchies for accurate object...

Muhammad Ghifary | Transfer LearningWeta Digital Ltd.

Outline1. Problem2. Teori

○ Terminologi○ Definisi & klasifikasi transfer learning

3. Review algoritma4. Scatter Component Analysis (SCA) - [TPAMI 2017]

○ Algoritma transfer learning untuk object recognition○ (Kernel) Principal Component Analysis○ Hasil eksperimen

5. Kesimpulan

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1. Problem

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1. Sketch-based face recognition

B. F. Klare, A. K. Jain. “Heterogenous Face Recognition using Kernel Prototype Similarities”, TPAMI 2013

Bagaimana komputer mencocokan gambar wajah asli dengan sketsa?

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Sketch

Original


1. Dataset biasA.Torralba & A. Efros. “Unbiased look at dataset bias”, CVPR 2011

SUN09 LabelMe PASCAL ImageNet Caltech101 MSRC Self Mean others

Percent drop

SUN09 69.8 50.7 42.2 42.6 54.7 69.4 69.8 51.9 26%

LabelMe 61.8 67.6 40.8 38.5 53.4 67.0 67.6 52.3 23%

PASCAL 55.8 55.2 62.1 56.8 54.2 74.8 62.1 59.4 4%

ImageNet 43.9 31.8 46.9 60.7 59.3 67.8 60.7 49.9 18%

Caltech101 20.2 18.8 11.0 31.4 100 29.3 100 22.2 78%

MSRC 28.6 17.1 32.3 21.5 67.7 74.3 74.3 33.4 55%

Mean others 42.0 34.7 34.6 38.2 57.9 61.7 72.4 44.8 48%

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1. Facial performance capture

source target

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1. Problem

● Data training tidak sepenuhnya mewakili realita/target ! -- domain mismatch● Tidak mudah untuk mengoleksi dan melakukan anotasi data training● Target domain tidak sepenuhnya diketahui

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Gambar diambil dari Y. Ganin & V. Lempitsky, “Unsupervised Domain Adaptation by Backpropagation”, ICML 2015

2. Teori

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2. Definisi transfer learning

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“Kemampuan sistem untuk mentransfer pengetahuan/skill yang telah didapatkan dari domain / task tertentu (source), untuk diaplikasikan di domain / task yang lain (target)”

● Psikologi/Neurobiologi:

Indonesian → Malay, Japanese → Korean, Karate → Tinju, Piano → Gitar, C++ → Java, MATLAB → Python (numpy), Math →{Computer Science, Physics, Economics, …}

● Machine learning○ mengidentifikasi kesamaan pola/atribut/fitur antar domain

● Domain:

● Task:

● Training data

2. Definisi transfer learning

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● Source domain & task:

● Target domain & task:

● Supervised learning:

● Transfer learning:


2. Klasifikasi transfer learning

1. Inductive transfer ( )○ Multi-task learning, self-taught learning, one-shot learning, zero-shot learning

2. Transductive transfer learning ( )○ Domain adaptation○ Domain generalization○ Covariate shift○ Sample selection bias

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S. J. Pan & Q. Yang. “A Survey on Transfer Learning”, TKDE 2009

3. Review Algoritma

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3. DASVM (1)

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SVM recap● Data training:

● Classifier:

● Formulasi:

L. Bruzzone, M. Marconcini, “Domain adaptation problems: a DASVM classification technique and a cricular validation strategy.”, TPAMI 2010

Sumber: https://epat2014.sciencesconf.org/conference/epat2014/pages/slides_DA_epat_17.pdf

https://epat2014.sciencesconf.org/conference/epat2014/pages/slides_DA_epat_17.pdf




3. DASVM (2) [Bruzzone & Marconcini, TPAMI 2010]

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3. Feature Augmentation

Daume-III, “Frustratingly easy domain adaptation”, ACL 2007

Li et al, “Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation”, TPAMI 2014

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3. Transfer Component Analysis

Memanfaatkan fungsi jarak antar distribusi probabilitas

● Maximum Mean Discrepancy:

● Formulasi

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Sumber: http://informationtransfereconomics.blogspot.co.nz/2015/05/the-economic-allocation-problem.html

http://informationtransfereconomics.blogspot.co.nz/2015/05/the-economic-allocation-problem.html




3. Geodesic Interpolation

Gopalan et al., “Domain adaptation for object recognition: an unsupervised approach”, ICCV 2011

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Gong et al., “Geodesic flow kernel for unsupervised domain adaptation”, CVPR 2012


3. Deep Learning

Donahue et al., “DeCAF: a deep convolutional activation feature for generic visual recognition”, ICML 2014Girshick et al., “Rich feature hierarchies for accurate object detection and semantic segmentation”, CVPR 2014Yosinski et al., “How transferable are features in deep neural networks”, NIPS 2014Long et al., “Learning transferable features with deep adaptation networks”, ICML 2015Ganin & Lempitsky, “Unsupervised domain adaptation through backpropagation”, ICML 2015Ghifary et al., “Deep reconstruction-classification networks for unsupervised domain adaptation”, ECCV 2016

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[Girshick et al. CVPR2014]

4. Scatter Component Analysis(SCA)

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M. Ghifary, D. Balduzzi, W. B. Kleijn, M. Zhang, “Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization”, TPAMI 2017

Preprint: https://arxiv.org/abs/1510.04373

https://arxiv.org/abs/1510.04373


4. SCA

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* persebaran = variance = scatter

Algoritma representation/feature learning yang bertujuan:1. Memaksimalkan persebaran data keseluruhan2. Meminimalkan persebaran data pada kelas yang sama3. Memaksimalkan persebaran data yang memiliki kelas berbeda4. Meminimalkan persebaran domain

Discriminative


4. SCA

SCA menggabungkan unsur yang ada pada Principal Component Analysis (PCA)*, Linear Discriminant Analysis (LDA), Maximum Mean Discrepancy (MMD), dan kernel trick.

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4. Principal Component Analysis (1)● Mereduksi dimensi data menjadi beberapa komponen● Komponen-komponen tersebut mempertahankan

informasi yang dimiliki data

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Sumber: http://www.nlpca.org/fig_pca_principal_component_analysis.png

http://www.nlpca.org/fig_pca_principal_component_analysis.png


4. PCA (2)

PCA berbentuk model linear

Goal:

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4. PCA (3)

Tulis

sehingga

PCA berbasis linear projection → matrix W ortogonal ( ) →

Asumsi , persebaran / variance dari Z dapat dinyatakan dengan

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4. PCA (4)

Optimisasi: hitung matriks W

Ekspansi Var(Z)

Bentuk Lagrangian

dimana variabel-variabel Lagrange

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4. PCA (5)

Hitung

Eigen-decomposition:

Eigen-decomposition dari covariance matrix input/data menghasilkan basis yang memaksimalkan !

Komputasi representasi/component scores:

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4. PCA (6)

(Trained) loadings/basis:

Diberikan sebuah data

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Training● Input

○ Pastikan memiliki rata-rata 0

● Bentuk matrix input ● Hitung covariance matrix

● Hitung eigen-decomposision:

Test:● Input● Ekstrak representasi/fitur/components:


4. PCA (7)

Python (numpy)

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Scikit-learn: http://scikit-learn.org/stable/

http://scikit-learn.org/stable/


4. Kernel PCA

B. Scholkopf, A. Smola, “Nonlinear Component Analysis as a Kernel Eigenvalue”, Neural Computation 1998

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4. PCA vs Kernel PCA

Sumber: http://2.bp.blogspot.com/_slrAR0IXTL0/TF-OZaNbRCI/AAAAAAAAAUo/SdYS3hXd4MI/s1600/figure.png

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http://2.bp.blogspot.com/_slrAR0IXTL0/TF-OZaNbRCI/AAAAAAAAAUo/SdYS3hXd4MI/s1600/figure.png


4. Scatter Component Analysis

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Muhammad Ghifary | Transfer LearningWeta Digital Ltd. 36

4. SCA: Experiments


4. SCA: Experiments

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4. SCA: ExperimentsOffice dataset [Saenko et al., ECCV2010]

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4. SCA: ExperimentsOffice dataset [Saenko et al., ECCV2010]

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5. Kesimpulan1. Transfer learning bertujuan untuk mengatasi problem ketidaksesuaian

antara data dan target/realita (domain mismatch)○ Pengoleksian data sulit dilakukan○ Target tidak diketahui secara pasti

2. Klasifikasi transfer learning○ Inductive transfer: multi-task learning, self-taught learning, one-shot learning, zero-shot

learing○ Transductive transfer: domain adaptation, domain generalization, covariate shift, sample

selection bias

3. Berbagai algoritma TL untuk aplikasi computer vision telah tersedia, namun masih open problem

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