label-noise robust generative adversarial networks · label-noise robust generative adversarial...
TRANSCRIPT
Takuhiro Kaneko1 Yoshitaka Ushiku1 Tatsuya Harada1, 2
1The University of Tokyo 2RIKEN
Noisy labeled Conditioned on clean labels
Training data rcGAN
Code
Label-Noise Robust Generative Adversarial Networks
Talk
Objective: Label-noise robust conditional image generation
2
Our goal is to construct a label-noise robust conditional image generatorthat can reproduce clean labeled data (a)even when noisy labeled data (b) are only available during the training.
(a) Clean labeled data (b) Noisy labeled data
AvailableUnavailable
Challenge: Limitation of naïve conditional generative models
3
Naïve conditional generative models (e.g., AC-GAN [1], cGAN [2, 3])attempt to construct a generator conditioned on observable labels(i.e., noisy labels in this case).
Noisy labeled Conditioned on noisy labels
Training data cGAN
[1] Odena et al. ICML 2017. [2] Mirza & Osindero. arXiv 2014. [3] Miyato & Koyama. ICLR 2018.
AC-GAN: auxiliary classifier GAN, cGAN: conditional GAN
Contribution: Proposal of label-noise robust GANs
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To overcome this limitation, we propose label-noise robust GANs (rGANs) thatcan construct a generator conditioned on clean labelseven when trained with noisy labeled data.
Conditioned on clean labels
rcGAN
Noisy labeled
Training data
rcGAN: label-noise robust conditional GAN
We incorporate a noise transition model into naïve conditional GANs [1, 2, 3].
Two variants
DDD/CD/C
Main idea: Incorporation of noise transition model
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Noise transition model
Clean labelNoisy label
AC-GAN [1] rAC-GAN cGAN [2, 3] rcGAN
[1] Odena et al. ICML 2017. [2] Mirza & Osindero. arXiv 2014. [3] Miyato & Koyama. ICLR 2018.
D/C
Auxiliary classifier GAN [1]
Generator:
Discriminator/Classifier: /
G
Baseline: AC-GAN
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Dataset
[1] Odena et al. ICML 2017.
D/C
Auxiliary classifier GAN [1]
Generator:
Discriminator/Classifier: /
Limitation
C can fit noisy labels when trained with noisy labeled data.
G
Baseline: AC-GAN
7
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Noisy label classifier
Noisy
Dataset
[1] Odena et al. ICML 2017.
Label-noise robust auxiliary classifier GAN
Generator:
Discriminator/Classifier: /
Solution
We correct the Cʼs prediction using the noise transition model (forward correction [4]).
G
D/C
Proposal: rAC-GAN
8
Clean label classifier
Clean
Dataset
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Xp(y|y)C(y|x)
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Noisy
[4] Patrini et al. CVPR 2017.
Incorporate
D
Conditional GAN [2, 3]
Generator:
Discriminator:
Baseline: cGAN
9
Dataset
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[2] Mirza & Osindero. arXiv 2014. [3] Miyato & Koyama. ICLR 2018.
G
D
Conditional GAN [2, 3]
Generator:
Discriminator:
Limitation
G is optimized conditioned on noisy labels when trained with noisy labeled data.
Baseline: cGAN
10
Dataset
Conditionedon noisy label
Noisy
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[2] Mirza & Osindero. arXiv 2014. [3] Miyato & Koyama. ICLR 2018.
G
Label-noise robust conditional GAN
Generator:
Discriminator: ( )
Solution
We correct the Dʼs input using the noise transition model.
yg ⇠ p(y|yg)<latexit sha1_base64="mYBu9DMzRUkQnQRSvMQ7XZ1Rx/E=">AAACIXicbZDLSsNAFIZPvNZ6q7p0MyiCIkjiRpdFNy4r2As0tUwm03ZwJgkzJ0KJeQZfQfAZ3OranbgTV76J0wuirQcGPv7/HM6ZP0ikMOi6H87M7Nz8wmJhqbi8srq2XtrYrJk41YxXWSxj3Qio4VJEvIoCJW8kmlMVSF4Pbs4Hfv2WayPi6Ar7CW8p2o1ERzCKVmqXDnwUMuRZP7/uEt8IRZL9H4ncEb9HcWgetEu77pE7LDIN3hh2yzv+4QMAVNqlLz+MWap4hExSY5qem2AroxoFkzwv+qnhCWU3tMubFiOquGllwy/lZM8qIenE2r4IyVD9PZFRZUxfBbZTUeyZSW8g/uc1U+yctjIRJSnyiI0WdVJJMCaDfEgoNGco+xYo08LeSliPasrQpvhnS6Bym4k3mcA01I6PPMuXNpwzGFUBtmEH9sGDEyjDBVSgCgzu4Qme4cV5dF6dN+d91DrjjGe24E85n983VKYU</latexit><latexit sha1_base64="N+GObZTuSN7epug/Sy8bqHdjL6w=">AAACIXicbZDLSgMxFIYzXmu9VV26CS1Ci1Bm3Oiy6MZlBXuBTi2ZNG1Dk5khOSMMY5/BjW/gM7jVtTtxV1z5JqbTIrb1QODj/8/hnPxeKLgG2x5bK6tr6xubma3s9s7u3n7u4LCug0hRVqOBCFTTI5oJ7rMacBCsGSpGpCdYwxteTfzGPVOaB/4txCFrS9L3eY9TAkbq5EoucNFlSTy662NXc4nD4q+EH7A7IJCapU6uYJfttPAyODMoVPLu6dO4Elc7uW+3G9BIMh+oIFq3HDuEdkIUcCrYKOtGmoWEDkmftQz6RDLdTtIvjfCJUbq4FyjzfMCp+nciIVLrWHqmUxIY6EVvIv7ntSLoXbQT7ocRMJ9OF/UigSHAk3xwlytGQcQGCFXc3IrpgChCwaQ4t8WTI5OJs5jAMtTPyo7hGxPOJZpWBh2jPCoiB52jCrpGVVRDFD2iF/SK3qxn6936sD6nrSvWbOYIzZX19QNXmKea</latexit><latexit sha1_base64="UmeAF4e1o7i8qOMDmJr5KPWFn0U=">AAACIXicbZDLSsNAFIYnXmu9VV26GSxCuymJG10W3bisYC/QxDKZTNuhM0mYORFC7DP4ED6DW127E3fiyjdxmhaxrQcGPv7/HM6Z348F12Dbn9bK6tr6xmZhq7i9s7u3Xzo4bOkoUZQ1aSQi1fGJZoKHrAkcBOvEihHpC9b2R1cTv33PlOZReAtpzDxJBiHvc0rASL1S1QUuApal47sBdjWXOK78SvgBu0MCuVntlcp2zc4LL4MzgzKaVaNX+naDiCaShUAF0brr2DF4GVHAqWDjoptoFhM6IgPWNRgSybSX5V8a41OjBLgfKfNCwLn6dyIjUutU+qZTEhjqRW8i/ud1E+hfeBkP4wRYSKeL+onAEOFJPjjgilEQqQFCFTe3YjokilAwKc5t8eXYZOIsJrAMrbOaY/jGLtcvZ+kU0DE6QRXkoHNUR9eogZqIokf0jF7Qq/VkvVnv1se0dcWazRyhubK+fgANzqSL</latexit>
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G(z, yg)<latexit sha1_base64="SOIwORCvydCa/EjDTfWAMeO4m6Y=">AAACDXicbZC7SgNBFIbPxluMt1WxshkShIgSdm20DFpoGcFcIBvD7GSSDJnZXWZmhbjsM/gK2mptJ7Y+g6Vv4uRSmMQfDnz85xzO4fcjzpR2nG8rs7S8srqWXc9tbG5t79i7ezUVxpLQKgl5KBs+VpSzgFY105w2Ikmx8Dmt+4OrUb/+QKViYXCnhxFtCdwLWJcRrI3Vtg+ui4nnC/SYniKvj3UyTO97x2274JScsdAiuFMolPPeyTMAVNr2j9cJSSxooAnHSjVdJ9KtBEvNCKdpzosVjTAZ4B5tGgywoKqVjN9P0ZFxOqgbSlOBRmP370aChVJD4ZtJgXVfzfdG5n+9Zqy7F62EBVGsaUAmh7oxRzpEoyxQh0lKNB8awEQy8ysifSwx0SaxmSu+SE0m7nwCi1A7K7mGb004lzBRFg4hD0Vw4RzKcAMVqAKBBF7gFd6sJ+vd+rA+J6MZa7qzDzOyvn4BUw2dCA==</latexit><latexit sha1_base64="Xk5dcECSM836Sq211VVs1O96jYM=">AAACDXicbZC7SgNBFIZn4y3G26pY2QwJQkQJuzZaBi20jGAukF3D7GSSDJmZXWZmhXXZZ7DxBWy1thNbnyGlb+IksTCJPxz4+M85nMMfRIwq7TgjK7e0vLK6ll8vbGxube/Yu3sNFcYSkzoOWShbAVKEUUHqmmpGWpEkiAeMNIPh1bjffCBS0VDc6SQiPkd9QXsUI22sjn1wXU69gMPH7BR6A6TTJLvvH3fsklNxJoKL4P5CqVr0Tp5H1aTWsb+9bohjToTGDCnVdp1I+ymSmmJGsoIXKxIhPER90jYoECfKTyfvZ/DIOF3YC6UpoeHE/buRIq5UwgMzyZEeqPne2Pyv145178JPqYhiTQSeHurFDOoQjrOAXSoJ1iwxgLCk5leIB0girE1iM1cCnplM3PkEFqFxVnEN35pwLsFUeXAIiqAMXHAOquAG1EAdYJCCF/AK3qwn6936sD6noznrd2cfzMj6+gFzUZ6O</latexit><latexit sha1_base64="fziiGSVr2MA5GrP28d7XcL5PqqA=">AAACDXicbZDLSsNAFIYn9VbrLSqu3AwWoYKUxI0uiy50WcFeoIllMp20Q2cmYWYixJBn8Bnc6tqduPUZXPomTtssbPWHAx//OYdz+IOYUaUd58sqLS2vrK6V1ysbm1vbO/buXltFicSkhSMWyW6AFGFUkJammpFuLAniASOdYHw16XceiFQ0Enc6jYnP0VDQkGKkjdW3D65rmRdw+JifQm+EdJbm98OTvl116s5U8C+4BVRBoWbf/vYGEU44ERozpFTPdWLtZ0hqihnJK16iSIzwGA1Jz6BAnCg/m76fw2PjDGAYSVNCw6n7eyNDXKmUB2aSIz1Si72J+V+vl+jwws+oiBNNBJ4dChMGdQQnWcABlQRrlhpAWFLzK8QjJBHWJrG5KwHPTSbuYgJ/oX1Wdw3fOtXGZZFOGRyCI1ADLjgHDXADmqAFMMjAM3gBr9aT9Wa9Wx+z0ZJV7OyDOVmfPymHm38=</latexit>
Proposal: rcGAN
11
G
Dataset
D
Conditionedon clean label
Incorporate
Clean Noisy
Experiment: Comprehensive study
12
Experimental conditions
Dataset: CIFAR-10 [5], CIFAR-100 [5]
Noise: Symmetric [6], Asymmetric [4] (Noise rate ∈ {0, 0.1, 0.3, 0.5, 0.7, 0.9})
GAN configurations: DCGAN [7], WGAN-GP [8], CT-GAN [9], SN-GAN [10]
Comparison: AC-GAN vs. rAC-GAN, cGAN vs. rcGANEvaluation metrics: FID [11], Intra FID [3], GAN-test [12], GAN-train [12]
We tested 336 conditions in total.
[5] Krizhevsky. 2009. [6] van Rooyen et al. NIPS 2015. [4] Patrini et al. CVPR 2017.[7] Radford et al. ICLR 2016. [8] Gulrajani et al. NIPS 2017. [9] Wei et al. ICLR 2018. [10] Miyato et al. ICLR 2018.
[11] Heusel et al. NIPS 2017. [3] Miyato & Koyama. ICLR 2018. [12] Shmelkov et al. ECCV 2018.
Experiment: Quantitative results
13
Comparison between the proposed model and the baselines across all the conditions
Baseline
Prop
osed
rAC-GAN vs. AC-GANrcGAN vs. cGAN
Larger is better
Baseline
Prop
osed
rAC-GAN vs. AC-GANrcGAN vs. cGAN
Larger is better
(a) GAN-test (b) GAN-train
The proposed models outperform the baselines in most cases.
Experiment: Qualitative results I
14
CIFAR-10 symmetric noise (uniform noise)
rAC-CT-GAN (30.2)AC-CT-GAN (36.4)
rcSN-GAN (28.6)cSN-GAN (72.0)
Baseline Proposed
Significantlydegraded
The number indicates Intra FID. A smaller value is better.
Experiment: Qualitative results II
15
CIFAR-10 asymmetric noise (class-dependent noise, e.g., cat ⇄ dog)
rAC-CT-GAN (27.2)AC-CT-GAN (45.7)
rcSN-GAN (25.6)cSN-GAN (33.7)
Baseline Proposed
The number indicates Intra FID. A smaller value is better.
Confusebetweenflipped classes
Cat Dog Cat Dog
Cat Dog Cat Dog
Further analyses
16
Effects of estimated noise transition modelWe examined the effect whenthe noise transition model is estimated from data [4].
Evaluation of improved techniqueWe validated the effect of an improved technique,which we developed to boost the performancein severely noisy setting.
Evaluation on real-world noiseWe tested on Clothing1M [13],which includes real-world noisy labeled data.
LMI = Ez⇠p(z),yg⇠p(y)
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¯
x
i= argmax
x2X 0C 0
(y = i|x)
T 0i,j = C 0
(y = j|¯xi)
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Robust two-step training algorithm [4]
Loss for improving the performance
[4] Patrini et al. CVPR 2017. [13] Xiao et al. CVPR 2015.
Qualitative results on Clothing1M
Thank you!
17
Our code is publicly available at https://github.com/takuhirok/rGAN/
PosterSession: Synthesis
#: 133Time: Tuesday 15:20-18:00
DDD/CD/C
AC-GAN [1] rAC-GAN cGAN [2, 3] rcGAN
[1] Odena et al. ICML 2017. [2] Mirza & Osindero. arXiv 2014. [3] Miyato & Koyama. ICLR 2018.