[dl輪読会]semi supervised learning with context-conditional generative adversarial networks

論文紹介: Semi-supervised learning with context-condi8onal genera8ve adversarial networks ICLR’17 under review Emily Denton et, al. 1

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Page 1: [Dl輪読会]semi supervised learning with context-conditional generative adversarial networks

論文紹介:Semi-supervisedlearningwithcontext-condi8onal

genera8veadversarialnetworks

ICLR’17underreviewEmilyDentonet,al.

Page 2: [Dl輪読会]semi supervised learning with context-conditional generative adversarial networks

書誌情報

•  Semi-supervisedlearningwithcontext-condi8onalgenera8veadversarialnetworks

•  EmilyDenton(NYU),SamGross,RobFergus(FacebookAIResearch)

•  ICLR’17underreview

•  pdf:hPp://openreview.net/pdf?id=BJ--gPcxl

Page 3: [Dl輪読会]semi supervised learning with context-conditional generative adversarial networks

内容

•  GANによるinpain8ng(画像復元) を応用したsemi-supervisedlearning手法

•  基本的に既存手法の組み合わせ

Page 4: [Dl輪読会]semi supervised learning with context-conditional generative adversarial networks

関連研究:ContextEncoder(CVPR’16)

•  Pathaketal.(2016)–  hPps://people.eecs.berkeley.edu/~pathak/context_encoder/

AdversarialLoss

JointLoss

L2Loss

x:imageF:context-encoderM:binarymask

Discriminatorは条件付けない(マスク境界で判断するように学習させないため)

c.f.GAN:

Page 6: [Dl輪読会]semi supervised learning with context-conditional generative adversarial networks

提案手法 (1)

•  CC-GAN(Context-Condi8onalGAN)

x:imagem:mask

CC-GAN^2(generatorで生成した画像をマスクしないものも入れる)

Context-encoderとの違い:context-encoder→inpain8ngに適した特徴量提案手法→real/fakeinpain8ngの区別に適した特徴量

Page 7: [Dl輪読会]semi supervised learning with context-conditional generative adversarial networks

提案手法 (2)

•  Semi-supervisedlearningwithCC-GAN(SSL-GAN)

ラベル有りデータの場合はclassifica8onlossも出す

Radfordetal.(ICLR’16)

Page 8: [Dl輪読会]semi supervised learning with context-conditional generative adversarial networks

ネットワーク構造

Page 9: [Dl輪読会]semi supervised learning with context-conditional generative adversarial networks

実験結果:STL-10

STL-10:96x96.10カテゴリ,各カテゴリにラベル付き1,000枚画像，その他100,000枚画像CC-GAN:96x96から64x64をrandomcrop,holeは32x32

Page 10: [Dl輪読会]semi supervised learning with context-conditional generative adversarial networks

実験結果:PASCALVOC

PASCALVOC2007:20クラス，5011枚CC-GAN:128x128,hole64x64,lowresolu8on32x32

Page 11: [Dl輪読会]semi supervised learning with context-conditional generative adversarial networks

実験結果:Inpain8ng

Page 12: [Dl輪読会]semi supervised learning with context-conditional generative adversarial networks

実験結果:Inpain8ng

Page 13: [Dl輪読会]semi supervised learning with context-conditional generative adversarial networks

まとめ

•  context-condi8onalなGANを用いたsemi-supervisedlearning手法の提案–  当然inpain8ngもできる–  ただしinpain8ng自体はcontext-encoderの方が良い結果

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