Robustness of Conditional GANs to Noisy Labels Spotlight presentation, NeurIPS 2018 Kiran K. Thekumparampil 1 Ashish Khetan 1 Zinan Lin 2 Sewoong Oh 1 1 University of Illinois at Urbana-Champaign 2 Carnegie Mellon University Poster #5, Tue, Dec 4 2018 Thekumparampil (UIUC) Robust Conditional GAN NeurIPS 2018 1 / 14
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Robustness of Conditional GANs to Noisy Labels04... · Spotlight presentation, NeurIPS 2018 Kiran K. Thekumparampil1 Ashish Khetan1 Zinan Lin2 Sewoong Oh1 1University of Illinois
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Robustness of Conditional GANs to Noisy LabelsSpotlight presentation, NeurIPS 2018
Kiran K. Thekumparampil1 Ashish Khetan1
Zinan Lin2 Sewoong Oh1
1University of Illinois at Urbana-Champaign
2Carnegie Mellon University
Poster #5, Tue, Dec 4 2018
Thekumparampil (UIUC) Robust Conditional GAN NeurIPS 2018 1 / 14
Conditional GAN (cGAN) is vital for achieving high quality
Thekumparampil (UIUC) Robust Conditional GAN NeurIPS 2018 8 / 14
RCGAN generates correct class (MNIST)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90.0
0.2
0.4
0.6
0.8
1.0
Noise Level
GeneratorLabel
Accuracy
−→ cGAN
Thekumparampil (UIUC) Robust Conditional GAN NeurIPS 2018 9 / 14
RCGAN generates correct class (MNIST)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90.0
0.2
0.4
0.6
0.8
1.0
Noise Level
GeneratorLabel
Accuracy
−→ cGAN
−→ RCGAN
Thekumparampil (UIUC) Robust Conditional GAN NeurIPS 2018 10 / 14
RCGAN generates correct class (MNIST)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90.0
0.2
0.4
0.6
0.8
1.0
Noise Level
GeneratorLabel
Accuracy
−→ cGAN
−→ RCGAN
−→ RCGAN-U
Thekumparampil (UIUC) Robust Conditional GAN NeurIPS 2018 11 / 14
RCGAN improves quality of samples (CIFAR-10)
0.0 0.2 0.4 0.6 0.87.5
7.6
7.7
7.8
7.9
8.0
8.1
8.2
Noise Level
InceptionScore
−→ cGAN
−→ RCGAN
−→ RCGAN-U
Thekumparampil (UIUC) Robust Conditional GAN NeurIPS 2018 12 / 14
RCGAN can correct noisy training labels (MNIST)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90.0
0.2
0.4
0.6
0.8
1.0
Noise Level
LabelRecoveryAccuracy
−→ cGAN
−→ RCGAN
−→ RCGAN-U
Thekumparampil (UIUC) Robust Conditional GAN NeurIPS 2018 13 / 14
Thank you
Poster #5, Tue, Dec 04https://github.com/POLane16/Robust-Conditional-GAN
[Arora 2015] S. Arora, R. Ge, Y. Liang, T. Ma, and Y. Zhang. Generalization and equilibrium ingenerative adversarial nets (GANs), ICML 2018.[Bora 2018] A. Bora, E. Price, and A. G. Dimakis. AmbientGAN: Generative models from lossymeasurements, ICLR, 2018.[Brock 2018] A. Brock, J. Donahue, and K. Simonyan. Large scale gan training for high fidelitynatural image synthesis, arXiv preprint arXiv:1809.11096.[Miyato 2018] T. Miyato, and M. Koyama. cGANs with projection discriminator. ICLR, 2018.[Sukhbaatar 2015] S. Sukhbaatar, J. Bruna, M. Paluri, L. Bourdev, and R. Fergus. Trainingconvolutional networks with noisy labels. In ICLR, Workshop, 2015.
Thekumparampil (UIUC) Robust Conditional GAN NeurIPS 2018 14 / 14