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angusturner.github.io | ||
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proceedings.neurips.cc
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sander.ai
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| | | | | More thoughts on diffusion guidance, with a focus on its geometry in the input space. | |
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yang-song.net
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| | | | | 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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runswiththedug.wordpress.com
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| | | Visit the post for more. | ||
