Explore >> Select a destination
|
You are here 
|
underscore.io | ||
| | | | |
sander.ai
|
|
| | | | | More thoughts on diffusion guidance, with a focus on its geometry in the input space. | |
| | | | |
tiao.io
|
|
| | | | | An in-depth practical guide to variational encoders from a probabilistic perspective. | |
| | | | |
www.depthfirstlearning.com
|
|
| | | | | [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... | |
| | | | |
liorsinai.github.io
|
|
| | | Denoising diffusion probabilistic models for AI art generation from first principles in Julia. This is part 1 of a 3 part series on these models. | ||
