/explore
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github.com | ||
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sander.ai
More thoughts on diffusion guidance, with a focus on its geometry in the input space. |
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yang-song.net
This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ... |
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christopher-beckham.github.io
Techniques for label conditioning in Gaussian denoising diffusion models |
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www.sebpartarrieu.com
Dive into VAEs with non Gaussian distributions |
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