“Denoising Diffusion Probabilistic Models”, 2020-06-19 (; backlinks; similar):
We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.
Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding.
On the unconditional CIFAR-10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256×256 LSUN, we obtain sample quality similar to Progressive GAN. Our implementation is available at Github.
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