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blog.evjang.com | ||
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www.depthfirstlearning.com
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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... | |
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sander.ai
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| | | | | Diffusion models have become very popular over the last two years. There is an underappreciated link between diffusion models and autoencoders. | |
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colinraffel.com
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sander.ai
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| | | Thoughts on the tension between iterative refinement as the thing that makes diffusion models work, and our continual attempts to make it _less_ iterative. | ||
