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Generative Adversarial Networks @ ICML 2019

The document reviews advancements in generative adversarial networks (GANs), focusing on models such as Progressive GAN, BigGAN, and StyleGAN. It outlines methodologies for high-fidelity image generation, including various techniques like spectral normalization, self-attention, and hierarchical latent spaces. The contributions of multiple authors and papers within this field are highlighted, emphasizing ongoing improvements and complex architectures that enhance image quality.

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ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019) 1 3 5 7 2 4 6 8
BigGAN (Brock et al., ICLR 2019) StyleGAN (Karras et al., CVPR 2019)
2010- : DeNA / 2011– : Mobage 2014- : DeNA Mobage : ( ) TokyoWebmining - - 2010 60 /Koichi Hamada (@hamadakoichi)
/Koichi Hamada (@hamadakoichi) 78 : : 102*0DeNA AI : TZ ... ./ 0 KD SL KA N O N KD SL K N W
4 02 0 5 . / 2 2/ 15 52 2:/ 21 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.
// . /
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.
BigGAN (Brock et al., ICLR 2019) StyleGAN (Karras et al., CVPR 2019)
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
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
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
BigGAN (Brock et al., ICLR 2019) StyleGAN (Karras et al., CVPR 2019)
BigGAN (Brock et al., ICLR 2019) StyleGAN (Karras et al., CVPR 2019)
https://twitter.com/goodfellow_ian/status/1060592303859916800
+ 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
+ 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
+ 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
+ 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
+ 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
+ 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
+ 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
Self-Attention Generative Adversarial Networks. Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena. arXiv: 1805.08318. In ICML 2019.
Self-Attention Generative Adversarial Networks. Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena. arXiv: 1805.08318. In ICML 2019.
+ 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.
Self-Attention Generative Adversarial Networks. Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena. arXiv: 1805.08318. In ICML 2019.
+ 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
+ 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.
Large Scale GAN Training for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
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
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
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)
(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.
(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.
(512x512) Large Scale GAN Training for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
49 Large Scale GAN Training for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
50 Large Scale GAN Training for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
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]
+ 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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
Flat Metric Minimization with Applications in Generative Modeling Thomas Möllenhoff, Daniel Cremers. arXiv:1905.04730. In ICML 2019.
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
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
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
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
77 878 12 7 /.0 .1 1DeNA AI : O * . A A TL K S :A A :L K
Generative Adversarial Networks @ ICML 2019

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Generative Adversarial Networks @ ICML 2019

  • 1.
  • 4.
    1 3 57 2 4 6 8
  • 5.
    ProgressiveGAN (Karras etal., ICLR 2018) BigGAN (Brock et al., ICLR 2019) 1 3 5 7 2 4 6 8
  • 6.
    BigGAN (Brock etal., ICLR 2019) StyleGAN (Karras et al., CVPR 2019)
  • 8.
    2010- : DeNA/ 2011– : Mobage 2014- : DeNA Mobage : ( ) TokyoWebmining - - 2010 60 /Koichi Hamada (@hamadakoichi)
  • 9.
    /Koichi Hamada (@hamadakoichi) 78: : 102*0DeNA AI : TZ ... ./ 0 KD SL KA N O N KD SL K N W
  • 10.
    4 02 05 . / 2 2/ 15 52 2:/ 21 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. IanJ. 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 etal., ICLR 2019) StyleGAN (Karras et al., CVPR 2019)
  • 20.
    Long Oral Oral High-FidelityImage 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-FidelityImage 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-FidelityImage 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 etal., ICLR 2019) StyleGAN (Karras et al., CVPR 2019)
  • 26.
    BigGAN (Brock etal., ICLR 2019) StyleGAN (Karras et al., CVPR 2019)
  • 27.
  • 28.
    + Spectral Normalizationon 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 Normalizationon 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 Normalizationon 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 Normalizationon 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 Normalizationon 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 Normalizationon 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 Normalizationon 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 AdversarialNetworks. Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena. arXiv: 1805.08318. In ICML 2019.
  • 36.
    Self-Attention Generative AdversarialNetworks. Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena. arXiv: 1805.08318. In ICML 2019.
  • 37.
    + Spectral Normalizationon 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 AdversarialNetworks. Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena. arXiv: 1805.08318. In ICML 2019.
  • 39.
    + Spectral Normalizationon 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 Normalizationon 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 GANTraining for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
  • 42.
    Large Scale GANTraining 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 GANTraining 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 GANTraining 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 GANTraining for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
  • 46.
    (512x512) Large Scale GANTraining for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
  • 47.
    (512x512) Large Scale GANTraining 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 GANTraining for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
  • 49.
    49 Large Scale GANTraining for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
  • 50.
    50 Large Scale GANTraining 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 Normalizationon 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 GenerationWith Fewer Labels. Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.
  • 54.
    High-Fidelity Image GenerationWith Fewer Labels. Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.
  • 55.
    High-Fidelity Image GenerationWith Fewer Labels. Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.
  • 56.
    High-Fidelity Image GenerationWith Fewer Labels. Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.
  • 57.
    High-Fidelity Image GenerationWith Fewer Labels. Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.
  • 58.
    High-Fidelity Image GenerationWith Fewer Labels. Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.
  • 59.
    High-Fidelity Image GenerationWith Fewer Labels. Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.
  • 60.
    High-Fidelity Image GenerationWith Fewer Labels. Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.
  • 61.
    High-Fidelity Image GenerationWith Fewer Labels. Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. arXiv:1903.02271. In ICML 2019.
  • 62.
    High-Fidelity Image GenerationWith 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-FidelityImage 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 Minimizationwith Applications in Generative Modeling Thomas Möllenhoff, Daniel Cremers. arXiv:1905.04730. In ICML 2019.
  • 66.
    Non-Parametric Priors ForGenerative 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 AdversarialNetworks 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-FidelityImage 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-FidelityImage 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.
    77 878 127 /.0 .1 1DeNA AI : O * . A A TL K S :A A :L K