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brandinho.github.io | ||
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christopher-beckham.github.io
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| | | | | Techniques for label conditioning in Gaussian denoising diffusion models | |
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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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sander.ai
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| | | | | Perspectives on diffusion, or how diffusion models are autoencoders, deep latent variable models, score function predictors, reverse SDE solvers, flow-based models, RNNs, and autoregressive models, all at once! | |
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www.marekrei.com
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| | | My previous post on summarising 57 research papers turned out to be quite useful for people working in this field, so it is about time... | ||
