Generative Adversarial Networks @ ICML 2019 Generative Adversarial Networks (GANs) @ ICML 2019の論文まとめ資料です。ICML/ICLR 2019読み会@DeNAでの発表資料です

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2 4 6 8

5. ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
1 3 5 7
2 4 6 8

6. BigGAN (Brock et al., ICLR 2019) StyleGAN (Karras et al., CVPR 2019)

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TokyoWebmining
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/Koichi Hamada (@hamadakoichi)

9. /Koichi Hamada (@hamadakoichi)
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Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks
Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018.

11. // . /

14. Generative Adversarial Nets.
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-
Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio.
arXiv:1406.2661. In NIPS 2014.

16. BigGAN (Brock et al., ICLR 2019) StyleGAN (Karras et al., CVPR 2019)

20. Long Oral Oral
High-Fidelity Image Generation 2 1 1
Latent space 2 1 1
Target Metrics Optimization 1 1
Training Methodology 3 3
Unified Treatment 3 3
Inference 2 2
Loss 2 2
Missing Data 2 2
Others
(Estimation, Regularization/Normalization, Domain
Translation/Adaptation, Uncertainty, User Modeling
Discrete Data)
5 5

21. Long Oral Oral
High-Fidelity Image Generation 2 1 1
Latent space 2 1 1
Target Metrics Optimization 1 1
Training Methodology 3 3
Unified Treatment 3 3
Inference 2 2
Loss 2 2
Missing Data 2 2
Others
(Estimation, Regularization/Normalization, Domain
Translation/Adaptation, Uncertainty, User Modeling
Discrete Data)
5 5

24. Long Oral Oral
High-Fidelity Image Generation 2 1 1
Latent space 2 1 1
Target Metrics Optimization 1 1
Training Methodology 3 3
Unified Treatment 3 3
Inference 2 2
Loss 2 2
Missing Data 2 2
Others
(Estimation, Regularization/Normalization, Domain
Translation/Adaptation, Uncertainty, User Modeling
Discrete Data)
5 5

25. BigGAN (Brock et al., ICLR 2019) StyleGAN (Karras et al., CVPR 2019)

26. BigGAN (Brock et al., ICLR 2019) StyleGAN (Karras et al., CVPR 2019)

27. https://twitter.com/goodfellow_ian/status/1060592303859916800

28. + Spectral Normalization on Generator
+ Self Attention
+ Two Time Scale Update Rule
Spectral Normalization on Discriminator
Projection Discriminator
SNGAN with Projection (Miyato+, ICLR’18)
SAGAN (Zhang+, ICML’19)
BigGAN (Brock+, ICLR’19)
+ Large Batch Size (256→2048)
+ Large Channel (64→96)
+ Shared Embedding
+ Hierarchical Latent Space
ACGAN (Oden+, ICML’17)
Auxiliary Classier
+ Orthogonal Regularization
+ Truncation Trick
+ First Singular Value Clamp
+ Zero-centered Gradient Penalty

29. + Spectral Normalization on Generator
+ Self Attention
+ Two Time Scale Update Rule
Spectral Normalization on Discriminator
Projection Discriminator
SNGAN with Projection (Miyato+, ICLR’18)
SAGAN (Zhang+, ICML’19)
BigGAN (Brock+, ICLR’19)
+ Large Batch Size (256→2048)
+ Large Channel (64→96)
+ Shared Embedding
+ Hierarchical Latent Space
ACGAN (Oden+, ICML’17)
Auxiliary Classier
S3GAN (Lucic+, ICML’19)
+ Synthesis with Inferred Labels
+ Semi- /Self Supervised Training
+ Orthogonal Regularization
+ Truncation Trick
+ First Singular Value Clamp
+ Zero-centered Gradient Penalty

30. + Spectral Normalization on Generator
+ Self Attention
+ Two Time Scale Update Rule
Spectral Normalization on Discriminator
Projection Discriminator
SNGAN with Projection (Miyato+, ICLR’18)
SAGAN (Zhang+, ICML’19)
BigGAN (Brock+, ICLR’19)
+ Large Batch Size (256→2048)
+ Large Channel (64→96)
+ Shared Embedding
+ Hierarchical Latent Space
ACGAN (Oden+, ICML’17)
Auxiliary Classier
S3GAN (Lucic+, ICML’19)
+ Synthesis with Inferred Labels
+ Semi- /Self Supervised Training
+ Orthogonal Regularization
+ Truncation Trick
+ First Singular Value Clamp
+ Zero-centered Gradient Penalty
ICLR’19/ICML’19

31. + Spectral Normalization on Generator
+ Self Attention
+ Two Time Scale Update Rule
Spectral Normalization on Discriminator
Projection Discriminator
SNGAN with Projection (Miyato+, ICLR’18)
SAGAN (Zhang+, ICML’19)
BigGAN (Brock+, ICLR’19)
+ Large Batch Size (256→2048)
+ Large Channel (64→96)
+ Shared Embedding
+ Hierarchical Latent Space
ACGAN (Oden+, ICML’17)
Auxiliary Classier
S3GAN (Lucic+, ICML’19)
+ Synthesis with Inferred Labels
+ Semi- /Self Supervised Training
+ Orthogonal Regularization
+ Truncation Trick
+ First Singular Value Clamp
+ Zero-centered Gradient Penalty
ICLR’19/ICML’19

32. + Spectral Normalization on Generator
+ Self Attention
+ Two Time Scale Update Rule
Spectral Normalization on Discriminator
Projection Discriminator
SNGAN with Projection (Miyato+, ICLR’18)
SAGAN (Zhang+, ICML’19)
BigGAN (Brock+, ICLR’19)
+ Large Batch Size (256→2048)
+ Large Channel (64→96)
+ Shared Embedding
+ Hierarchical Latent Space
ACGAN (Oden+, ICML’17)
Auxiliary Classier
S3GAN (Lucic+, ICML’19)
+ Synthesis with Inferred Labels
+ Semi- /Self Supervised Training
+ Orthogonal Regularization
+ Truncation Trick
+ First Singular Value Clamp
+ Zero-centered Gradient Penalty
ICLR’19/ICML’19

33. + Spectral Normalization on Generator
+ Self Attention
+ Two Time Scale Update Rule
Spectral Normalization on Discriminator
Projection Discriminator
SNGAN with Projection (Miyato+, ICLR’18)
SAGAN (Zhang+, ICML’19)
BigGAN (Brock+, ICLR’19)
+ Large Batch Size (256→2048)
+ Large Channel (64→96)
+ Shared Embedding
+ Hierarchical Latent Space
ACGAN (Oden+, ICML’17)
Auxiliary Classier
S3GAN (Lucic+, ICML’19)
+ Synthesis with Inferred Labels
+ Semi- /Self Supervised Training
+ Orthogonal Regularization
+ Truncation Trick
+ First Singular Value Clamp
+ Zero-centered Gradient Penalty
ICLR’19/ICML’19

34. + Spectral Normalization on Generator
+ Self Attention
+ Two Time Scale Update Rule
Spectral Normalization on Discriminator
Projection Discriminator
SNGAN with Projection (Miyato+, ICLR’18)
SAGAN (Zhang+, ICML’19)
BigGAN (Brock+, ICLR’19)
+ Large Batch Size (256→2048)
+ Large Channel (64→96)
+ Shared Embedding
+ Hierarchical Latent Space
ACGAN (Oden+, ICML’17)
Auxiliary Classier
S3GAN (Lucic+, ICML’19)
+ Synthesis with Inferred Labels
+ Semi- /Self Supervised Training
+ Orthogonal Regularization
+ Truncation Trick
+ First Singular Value Clamp
+ Zero-centered Gradient Penalty
ICLR’19/ICML’19

35. Self-Attention Generative Adversarial Networks.
Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena. arXiv: 1805.08318. In ICML 2019.

36. Self-Attention Generative Adversarial Networks.
Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena. arXiv: 1805.08318. In ICML 2019.

37. + Spectral Normalization on Generator
+ Two Time Scale Update Rule (Heusel+, NeuIPS’17)
Learning Rate - Discriminator: Generator = 4:1
Self-Attention Generative Adversarial Networks.
Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena. arXiv: 1805.08318. In ICML 2019.

38. Self-Attention Generative Adversarial Networks.
Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena. arXiv: 1805.08318. In ICML 2019.

39. + Spectral Normalization on Generator
+ Self Attention
+ Two Time Scale Update Rule
Spectral Normalization on Discriminator
Projection Discriminator
SNGAN with Projection (Miyato+, ICLR’18)
SAGAN (Zhang+, ICML’19)
BigGAN (Brock+, ICLR’19)
+ Large Batch Size (256→2048)
+ Large Channel (64→96)
+ Shared Embedding
+ Hierarchical Latent Space
ACGAN (Oden+, ICML’17)
Auxiliary Classier
S3GAN (Lucic+, ICML’19)
+ Synthesis with Inferred Labels
+ Semi- /Self Supervised Training
+ Orthogonal Regularization
+ Truncation Trick
+ First Singular Value Clamp
+ Zero-centered Gradient Penalty
ICLR’19/ICML’19

40. + Spectral Normalization on Generator
+ Self Attention
+ Two Time Scale Update Rule (Heusel+NIPS’17)
(512x512)
+ Spectral Normalization on Discriminator
+ Projection Discriminator
SNGAN with Projection (Miyato+, ICLR’18)
SAGAN (Zhang+, ICML’19)
BigGAN (Brock+, ICLR’19)
+ Large Batch Size (256→2048)
+ Large Channel (64→96)
+ Shared Embedding
+ Hierarchical Latent Space
+ Truncation Trick
+ Orthogonal Regularization
+ First Singular Value Clamp
+ Zero-centered Gradient Penalty
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.

41. Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.

42. Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
Typical Architecture Res Block up
Res Block down
4. Truncation Trick
2. Shared Embedding
3. Orthogonal Regularization
(without diagonal terms)
5. First Singular Value Clamp
Z sampling
6. Zero-centered Gradient Penalty
Spectral norm
Generator
Discriminator
1. Hierarchical Latent Space
Architecture for
ImageNet at 512x512

43. Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
Typical Architecture Res Block up
Res Block down
4. Truncation Trick
2. Shared Embedding
3. Orthogonal Regularization
(without diagonal terms)
5. First Singular Value Clamp
Z sampling
6. Zero-centered Gradient Penalty
Spectral norm
Architecture for
ImageNet at 512x512
Generator
Discriminator
1. Hierarchical Latent Space
BigGAN - deep

44. Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
Inception Score
SNGAN
SAGAN
BiGGAN
BiGGAN-Deep
30 140 250
42.5
25.0
5.0
FID
FID vs Inception Score at 128x128FID / Inception Score (without Truncation)

45. (512x512)
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.

46. (512x512)
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.

47. (512x512)
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
(512x512)
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.

48. (512x512)
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.

49. 49
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.

50. 50
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.

51. 51
Progressive Structure-conditional GANs (PSGAN)
Full-body High-resolution Anime Generation with Progressive Structure-conditional
Generative Adversarial Networks.
Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida.
arXiv:1809.01890. In ECCV Workshop 2018.
// . 0/0 https://youtu.be/MXWm6w4E5q0
Semantic Image Synthesis with Spatially-Adaptive Normalization.
Taesung Park, Ming-Yu Liu, Ting-Chun Wang, Jun-Yan Zhu.
arXiv:1903.07291. In CVPR 2019.
SPatially-Adaptive (DE)normalization (SPADE) [GauGAN]

52. + Spectral Normalization on Generator
+ Self Attention
+ Two Time Scale Update Rule
Spectral Normalization on Discriminator
Projection Discriminator
SNGAN with Projection (Miyato+, ICLR’18)
SAGAN (Zhang+, ICML’19)
BigGAN (Brock+, ICLR’19)
+ Large Batch Size (256→2048)
+ Large Channel (64→96)
+ Shared Embedding
+ Hierarchical Latent Space
ACGAN (Oden+, ICML’17)
Auxiliary Classier
S3GAN (Lucic+, ICML’19)
+ Synthesis with Inferred Labels
+ Semi- /Self Supervised Training
+ Orthogonal Regularization
+ Truncation Trick
+ First Singular Value Clamp
+ Zero-centered Gradient Penalty
ICLR’19/ICML’19

53. High-Fidelity Image Generation With Fewer Labels.
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.

54. High-Fidelity Image Generation With Fewer Labels.
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.

55. High-Fidelity Image Generation With Fewer Labels.
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.

56. High-Fidelity Image Generation With Fewer Labels.
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.

57. High-Fidelity Image Generation With Fewer Labels.
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.

58. High-Fidelity Image Generation With Fewer Labels.
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.

59. High-Fidelity Image Generation With Fewer Labels.
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.

60. High-Fidelity Image Generation With Fewer Labels.
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.

61. High-Fidelity Image Generation With Fewer Labels.
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.

62. High-Fidelity Image Generation With Fewer Labels.
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.

64. Long Oral Oral
High-Fidelity Image Generation 2 1 1
Latent space 2 1 1
Target Metrics Optimization 1 1
Training Methodology 3 3
Unified Treatment 3 3
Inference 2 2
Loss 2 2
Missing Data 2 2
Others
(Estimation, Regularization/Normalization, Domain
Translation/Adaptation, Uncertainty, User Modeling
Discrete Data)
5 5

65. Flat Metric Minimization with Applications in Generative Modeling
Thomas Möllenhoff, Daniel Cremers. arXiv:1905.04730. In ICML 2019.

66. Non-Parametric Priors For Generative Adversarial Networks.
Rajhans Singh, Pavan Turaga, Suren Jayasuriya, Ravi Garg, Martin W. Braun. arXiv:1905.07061. In ICML 2019.
Interpolation
Inception Score / FID
Non-Prarametric Prior

67. MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
Szu-Wei Fu, Chien-Feng Liao, Yu Tsao, Shou-De Lin. arXiv:1905.04874. In ICML 2019.
Discriminator
Generator
Learning Curve of Objective Function
(Validation set)
(S )
Evaluation

68. Long Oral Oral
High-Fidelity Image Generation 2 1 1
Latent space 2 1 1
Target Metrics Optimization 1 1
Training Methodology 3 3
Unified Treatment 3 3
Inference 2 2
Loss 2 2
Missing Data 2 2
Others
(Estimation, Regularization/Normalization, Domain
Translation/Adaptation, Uncertainty, User Modeling
Discrete Data)
5 5

69. Long Oral Oral
High-Fidelity Image Generation 2 1 1
Latent space 2 1 1
Target Metrics Optimization 1 1
Training Methodology 3 3
Unified Treatment 3 3
Inference 2 2
Loss 2 2
Missing Data 2 2
Others
(Estimation, Regularization/Normalization, Domain
Translation/Adaptation, Uncertainty, User Modeling
Discrete Data)
5 5

71. 77 878 12 7 /.0 .1 1DeNA AI :
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