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 colinraffel.com
GANs and Divergence Minimization
| | distill.pub
Open Questions about Generative Adversarial Networks
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| | What we'd like to find out about GANs that we don't know yet.
| | dennybritz.com
Deep Learning ideas that have stood the test of time · Denny's Blog
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| | Deep Learning is such a fast-moving field and the huge number of research papers and ideas can be overwhelming.
| | www.depthfirstlearning.com
Wasserstein GAN · Depth First Learning
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| | [AI summary] The provided text is a comprehensive set of notes and exercises covering various topics in Generative Adversarial Networks (GANs) and their improvements, including standard GANs, Wasserstein GANs (WGANs), and WGAN with Gradient Penalty (WGAN-GP). The content includes theoretical explanations, practical implementation tasks, and discussion of challenges and solutions in training GANs. Key topics include the mathematical foundations of GANs, the limitations of standard GANs (such as mode collapse and sensitivity to hyperparameters), the introduction of WGANs to address these issues through the Wasserstein distance, and further improvements with WGAN-GP to mitigate problems like weight clipping instability. The text also includes exercises for calc...
| | github.com
GitHub - yang-song/score_sde: Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral) · GitHub
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| Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral) - yang-song/score_sde