- See Also
-
Links
- “Idempotent Generative Network”, Shocher et al 2023
- “A Cookbook of Self-Supervised Learning”, Balestriero et al 2023
- “BigVGAN: A Universal Neural Vocoder With Large-Scale Training”, Lee et al 2022
- “Diffusion-GAN: Training GANs With Diffusion”, Wang et al 2022
- “InvGAN: Invertable GANs”, Ghosh et al 2021
- “FuseDream: Training-Free Text-to-Image Generation With Improved CLIP+GAN Space Optimization”, Liu et al 2021
- “CDM: Cascaded Diffusion Models for High Fidelity Image Generation”, Ho et al 2021
- “Training GANs With Stronger Augmentations via Contrastive Discriminator (ContraD)”, Jeong & Shin 2021
- “TransGAN: Two Transformers Can Make One Strong GAN”, Jiang et al 2021
- “Contrastive Representation Learning: A Framework and Review”, Le-Khac et al 2020
- “Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis”, Anonymous 2020
- “Differentiable Augmentation for Data-Efficient GAN Training”, Zhao et al 2020
- “StyleGAN2-ADA: Training Generative Adversarial Networks With Limited Data”, Karras et al 2020
- “On Data Augmentation for GAN Training”, Tran et al 2020
- “Image Augmentations for GAN Training”, Zhao et al 2020
- “Anime Crop Datasets: Faces, Figures, & Hands”, Branwen et al 2020
- “Practical Aspects of StyleGAN2 Training”, l4rz 2020
- “A U-Net Based Discriminator for Generative Adversarial Networks”, Schönfeld et al 2020
- “Improved Consistency Regularization for GANs”, Zhao et al 2020
- “Improved Consistency Regularization for GANs § 2.1 Balanced Consistency Regularization (bCR)”, Zhao 2020 (page 2 org google)
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- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“Idempotent Generative Network”, Shocher et al 2023
“A Cookbook of Self-Supervised Learning”, Balestriero et al 2023
“BigVGAN: A Universal Neural Vocoder With Large-Scale Training”, Lee et al 2022
“BigVGAN: A Universal Neural Vocoder with Large-Scale Training”
“Diffusion-GAN: Training GANs With Diffusion”, Wang et al 2022
“InvGAN: Invertable GANs”, Ghosh et al 2021
“FuseDream: Training-Free Text-to-Image Generation With Improved CLIP+GAN Space Optimization”, Liu et al 2021
“FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization”
“CDM: Cascaded Diffusion Models for High Fidelity Image Generation”, Ho et al 2021
“CDM: Cascaded Diffusion Models for High Fidelity Image Generation”
“Training GANs With Stronger Augmentations via Contrastive Discriminator (ContraD)”, Jeong & Shin 2021
“Training GANs with Stronger Augmentations via Contrastive Discriminator (ContraD)”
“TransGAN: Two Transformers Can Make One Strong GAN”, Jiang et al 2021
“Contrastive Representation Learning: A Framework and Review”, Le-Khac et al 2020
“Contrastive Representation Learning: A Framework and Review”
“Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis”, Anonymous 2020
“Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis”
“Differentiable Augmentation for Data-Efficient GAN Training”, Zhao et al 2020
“Differentiable Augmentation for Data-Efficient GAN Training”
“StyleGAN2-ADA: Training Generative Adversarial Networks With Limited Data”, Karras et al 2020
“StyleGAN2-ADA: Training Generative Adversarial Networks with Limited Data”
“On Data Augmentation for GAN Training”, Tran et al 2020
“Image Augmentations for GAN Training”, Zhao et al 2020
“Anime Crop Datasets: Faces, Figures, & Hands”, Branwen et al 2020
“Practical Aspects of StyleGAN2 Training”, l4rz 2020
“A U-Net Based Discriminator for Generative Adversarial Networks”, Schönfeld et al 2020
“A U-Net Based Discriminator for Generative Adversarial Networks”
“Improved Consistency Regularization for GANs”, Zhao et al 2020
“Improved Consistency Regularization for GANs § 2.1 Balanced Consistency Regularization (bCR)”, Zhao 2020 (page 2 org google)
“Improved Consistency Regularization for GANs § 2.1 Balanced Consistency Regularization (bCR)”
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
stylized-gans
augmentation
generative-models
Wikipedia
Miscellaneous
Link Bibliography
-
https://arxiv.org/abs/2206.04658#nvidia: “BigVGAN: A Universal Neural Vocoder With Large-Scale Training”, Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon -
https://arxiv.org/abs/2112.01573: “FuseDream: Training-Free Text-to-Image Generation With Improved CLIP+GAN Space Optimization”, Xingchao Liu, Chengyue Gong, Lemeng Wu, Shujian Zhang, Hao Su, Qiang Liu -
https://cascaded-diffusion.github.io/: “CDM: Cascaded Diffusion Models for High Fidelity Image Generation”, Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, Tim Salimans -
https://arxiv.org/abs/2102.07074: “TransGAN: Two Transformers Can Make One Strong GAN”, Yifan Jiang, Shiyu Chang, Zhangyang Wang -
https://arxiv.org/abs/2006.10738: “Differentiable Augmentation for Data-Efficient GAN Training”, Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han -
crop: “Anime Crop Datasets: Faces, Figures, & Hands”, Gwern Branwen, Arfafax, Shawn Presser, Anonymous, Danbooru Community -
https://arxiv.org/abs/2002.12655: “A U-Net Based Discriminator for Generative Adversarial Networks”, Edgar Schönfeld, Bernt Schiele, Anna Khoreva -
https://arxiv.org/abs/2002.04724: “Improved Consistency Regularization for GANs”, Zhengli Zhao, Sameer Singh, Honglak Lee, Zizhao Zhang, Augustus Odena, Han Zhang