- See Also
-
Links
- “Optimal Transport-based Unsupervised Semantic Disentanglement: A Novel Approach for Efficient Image Editing in GANs”, Liu et al 2023d
- “Diffusion Models Beat GANs on Image Classification”, Mukhopadhyay et al 2023
- “Rosetta Neurons: Mining the Common Units in a Model Zoo”, Dravid et al 2023
- “Exposing Flaws of Generative Model Evaluation Metrics and Their Unfair Treatment of Diffusion Models”, Stein et al 2023
- “Generalizable Synthetic Image Detection via Language-guided Contrastive Learning”, Wu et al 2023
- “KD-DLGAN: Data Limited Image Generation via Knowledge Distillation”, Cui et al 2023
- “Generalizing Factorization of GANs by Characterizing Convolutional Layers”, Wang et al 2022b
- “PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression”, Vo et al 2022
- “Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values”, Humayun et al 2022
- “BigDatasetGAN: Synthesizing ImageNet With Pixel-wise Annotations”, Li et al 2022
- “Scatterbrain: Unifying Sparse and Low-rank Attention Approximation”, Chen et al 2021
- “Telling Creative Stories Using Generative Visual Aids”, Ali & Parikh 2021
- “DP-LaSE: Discovering Density-Preserving Latent Space Walks in GANs for Semantic Image Transformations”, Li et al 2021b
- “CDM: Cascaded Diffusion Models for High Fidelity Image Generation”, Ho et al 2021
- “Diffusion Models Beat GANs on Image Synthesis”, Dhariwal & Nichol 2021
- “MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains”, Wang et al 2021
- “Generating Images from Caption and vice Versa via CLIP-Guided Generative Latent Space Search”, Galatolo et al 2021
- “Data Instance Prior for Transfer Learning in GANs”, Mangla et al 2020
- “Network-to-Network Translation With Conditional Invertible Neural Networks”, Rombach et al 2020
- “SMYRF: Efficient Attention Using Asymmetric Clustering”, Daras et al 2020
- “Not-so-BigGAN: Generating High-Fidelity Images on Small Compute With Wavelet-based Super-Resolution”, Han et al 2020
- “Evolving Normalization-Activation Layers”, Liu et al 2020
- “GANSpace: Discovering Interpretable GAN Controls”, Härkönen et al 2020
- “A U-Net Based Discriminator for Generative Adversarial Networks”, Schönfeld et al 2020
- “Improved Consistency Regularization for GANs”, Zhao et al 2020
- “MineGAN: Effective Knowledge Transfer from GANs to Target Domains With Few Images”, Wang et al 2019
- “Detecting GAN Generated Errors”, Zhu et al 2019
- “Artbreeder”, Simon 2019
- “BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis § 4.2 Characterizing Instability: The Discriminator”, Brock et al 2019 (page 6 org deepmind)
- “Large Scale Adversarial Representation Learning”, Donahue & Simonyan 2019
- “Improved Precision and Recall Metric for Assessing Generative Models”, Kynkäänniemi et al 2019
- “Anime Neural Net Graveyard”, Gwern 2019
- “Making Anime With BigGAN”, Gwern 2019
- “Discriminator Rejection Sampling”, Azadi et al 2018
- “BigGAN: Large Scale GAN Training For High Fidelity Natural Image Synthesis § 5.2 Additional Evaluation On JFT-300M”, Brock et al 2018 (page 8 org deepmind)
- “Large Scale GAN Training for High Fidelity Natural Image Synthesis”, Brock et al 2018
- “The Unusual Effectiveness of Averaging in GAN Training”, Yazıcı et al 2018
- “Self-Attention Generative Adversarial Networks”, Zhang et al 2018
- “Spectral Norm Regularization for Improving the Generalizability of Deep Learning”, Yoshida & Miyato 2017
- Sort By Magic
- Miscellaneous
- Link Bibliography
See Also
Links
“Optimal Transport-based Unsupervised Semantic Disentanglement: A Novel Approach for Efficient Image Editing in GANs”, Liu et al 2023d
“Diffusion Models Beat GANs on Image Classification”, Mukhopadhyay et al 2023
“Rosetta Neurons: Mining the Common Units in a Model Zoo”, Dravid et al 2023
“Exposing Flaws of Generative Model Evaluation Metrics and Their Unfair Treatment of Diffusion Models”, Stein et al 2023
“Generalizable Synthetic Image Detection via Language-guided Contrastive Learning”, Wu et al 2023
“Generalizable Synthetic Image Detection via Language-guided Contrastive Learning”
“KD-DLGAN: Data Limited Image Generation via Knowledge Distillation”, Cui et al 2023
“KD-DLGAN: Data Limited Image Generation via Knowledge Distillation”
“Generalizing Factorization of GANs by Characterizing Convolutional Layers”, Wang et al 2022b
“Generalizing Factorization of GANs by Characterizing Convolutional Layers”
“PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression”, Vo et al 2022
“Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values”, Humayun et al 2022
“BigDatasetGAN: Synthesizing ImageNet With Pixel-wise Annotations”, Li et al 2022
“BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations”
“Scatterbrain: Unifying Sparse and Low-rank Attention Approximation”, Chen et al 2021
“Scatterbrain: Unifying Sparse and Low-rank Attention Approximation”
“Telling Creative Stories Using Generative Visual Aids”, Ali & Parikh 2021
“DP-LaSE: Discovering Density-Preserving Latent Space Walks in GANs for Semantic Image Transformations”, Li et al 2021b
“CDM: Cascaded Diffusion Models for High Fidelity Image Generation”, Ho et al 2021
“CDM: Cascaded Diffusion Models for High Fidelity Image Generation”
“Diffusion Models Beat GANs on Image Synthesis”, Dhariwal & Nichol 2021
“MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains”, Wang et al 2021
“MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains”
“Generating Images from Caption and vice Versa via CLIP-Guided Generative Latent Space Search”, Galatolo et al 2021
“Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search”
“Data Instance Prior for Transfer Learning in GANs”, Mangla et al 2020
“Network-to-Network Translation With Conditional Invertible Neural Networks”, Rombach et al 2020
“Network-to-Network Translation with Conditional Invertible Neural Networks”
“SMYRF: Efficient Attention Using Asymmetric Clustering”, Daras et al 2020
“Not-so-BigGAN: Generating High-Fidelity Images on Small Compute With Wavelet-based Super-Resolution”, Han et al 2020
“Evolving Normalization-Activation Layers”, Liu et al 2020
“GANSpace: Discovering Interpretable GAN Controls”, Härkönen et al 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
“MineGAN: Effective Knowledge Transfer from GANs to Target Domains With Few Images”, Wang et al 2019
“MineGAN: effective knowledge transfer from GANs to target domains with few images”
“Detecting GAN Generated Errors”, Zhu et al 2019
“Artbreeder”, Simon 2019
“BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis § 4.2 Characterizing Instability: The Discriminator”, Brock et al 2019 (page 6 org deepmind)
“Large Scale Adversarial Representation Learning”, Donahue & Simonyan 2019
“Improved Precision and Recall Metric for Assessing Generative Models”, Kynkäänniemi et al 2019
“Improved Precision and Recall Metric for Assessing Generative Models”
“Anime Neural Net Graveyard”, Gwern 2019
“Making Anime With BigGAN”, Gwern 2019
“Discriminator Rejection Sampling”, Azadi et al 2018
“BigGAN: Large Scale GAN Training For High Fidelity Natural Image Synthesis § 5.2 Additional Evaluation On JFT-300M”, Brock et al 2018 (page 8 org deepmind)
“Large Scale GAN Training for High Fidelity Natural Image Synthesis”, Brock et al 2018
“Large Scale GAN Training for High Fidelity Natural Image Synthesis”
“The Unusual Effectiveness of Averaging in GAN Training”, Yazıcı et al 2018
“Self-Attention Generative Adversarial Networks”, Zhang et al 2018
“Spectral Norm Regularization for Improving the Generalizability of Deep Learning”, Yoshida & Miyato 2017
“Spectral Norm Regularization for Improving the Generalizability of Deep Learning”
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.
latent-space
biggan
synthetic-image
gan-evaluation
Miscellaneous
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/doc/ai/nn/gan/biggan/2019-06-03-gwern-biggan-danbooru1k-256px.mp4 -
/doc/ai/nn/gan/biggan/2019-05-27-gwern-biggan-imagenetdanboorutransferlearning.mp4 -
/doc/ai/nn/gan/biggan/2019-01-17-gwern-biggantensorflow-animefaces-4100.jpg -
/doc/ai/nn/gan/biggan/2018-12-24-gwern-bigganpytorch-animefaces-1006320.jpg -
/doc/ai/nn/gan/biggan/2018-11-30-gwern-bigganpytorch-1kcharacters-314000.png -
/doc/ai/nn/gan/biggan/2018-gwern-brock-biggandeep-architecture.png -
https://twitter.com/RiversHaveWings/status/1586396490704261121 -
https://www.reddit.com/r/SpiceandWolf/comments/bx764z/im_sorry_i_had_to/
Link Bibliography
-
2023-liu-4.pdf: “Optimal Transport-based Unsupervised Semantic Disentanglement: A Novel Approach for Efficient Image Editing in GANs”, Yunqi Liu, Xue Ouyang, Tian Jiang, Hongwei Ding, Xiaohui Cui -
https://arxiv.org/abs/2306.09346: “Rosetta Neurons: Mining the Common Units in a Model Zoo”, Amil Dravid, Yossi Gandelsman, Alexei A. Efros, Assaf Shocher -
2022-wang-2.pdf: “Generalizing Factorization of GANs by Characterizing Convolutional Layers”, Yuehui Wang, Qing Wang, Dongyu Zhang -
https://arxiv.org/abs/2203.01993: “Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values”, Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk -
https://arxiv.org/abs/2201.04684: “BigDatasetGAN: Synthesizing ImageNet With Pixel-wise Annotations”, Daiqing Li, Huan Ling, Seung Wook Kim, Karsten Kreis, Adela Barriuso, Sanja Fidler, Antonio Torralba -
https://arxiv.org/abs/2110.15343#facebook: “Scatterbrain: Unifying Sparse and Low-rank Attention Approximation”, Beidi Chen, Tri Dao, Eric Winsor, Zhao Song, Atri Rudra, Christopher Ré -
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/2105.05233#openai: “Diffusion Models Beat GANs on Image Synthesis”, Prafulla Dhariwal, Alex Nichol -
https://arxiv.org/abs/2104.13742: “MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains”, Yaxing Wang, Abel Gonzalez-Garcia, Chenshen Wu, Luis Herranz, Fahad Shahbaz Khan, Shangling Jui, Joost van de Weijer -
https://arxiv.org/abs/2102.01645: “Generating Images from Caption and vice Versa via CLIP-Guided Generative Latent Space Search”, Federico A. Galatolo, Mario G. C. A. Cimino, Gigliola Vaglini -
https://papers.nips.cc/paper/2020/file/1cfa81af29c6f2d8cacb44921722e753-Paper.pdf: “Network-to-Network Translation With Conditional Invertible Neural Networks”, Robin Rombach, Patrick Esser, Björn Omme -
https://arxiv.org/abs/2010.05315: “SMYRF: Efficient Attention Using Asymmetric Clustering”, Giannis Daras, Nikita Kitaev, Augustus Odena, Alexandros G. Dimakis -
https://arxiv.org/abs/2009.04433: “Not-so-BigGAN: Generating High-Fidelity Images on Small Compute With Wavelet-based Super-Resolution”, Seungwook Han, Akash Srivastava, Cole Hurwitz, Prasanna Sattigeri, David D. Cox -
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 -
https://www.artbreeder.com/: “Artbreeder”, Joel Simon -
face-graveyard: “Anime Neural Net Graveyard”, Gwern -
biggan: “Making Anime With BigGAN”, Gwern -
https://arxiv.org/abs/1806.04498: “The Unusual Effectiveness of Averaging in GAN Training”, Yasin Yazıcı, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, Vijay Chandrasekhar