Denoising Diffusion Probabilistic Models

• URL: http://arxiv.org/abs/2006.11239v2
• Date: Wed, 16 Dec 2020 21:15:05 GMT
• Title: Denoising Diffusion Probabilistic Models
• Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel
• Abstract summary: We present high quality image synthesis results using diffusion probabilistic models. 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.
• Score: 91.94962645056896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion

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