Скачать или смотреть Denoising Diffusion Probabilistic Models (Jun 2020)

Title: Denoising Diffusion Probabilistic Models
Link: https://arxiv.org/abs/2006.11239
Date: 19 Jun 2020
Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel

Summary:

This research paper presents a significant advancement in the field of diffusion probabilistic models, a type of generative model inspired by nonequilibrium thermodynamics. The authors demonstrate the potential of these models for generating high-quality images, surpassing even the results achieved by generative adversarial networks (GANs) in some cases. They introduce a novel connection between diffusion models and denoising score matching, revealing a simplified and weighted variational bound objective. This equivalence facilitates the training of diffusion models, leading to enhanced sample quality. The authors also explore the concept of progressive lossy compression, highlighting the inherent inductive bias of diffusion models that makes them effective lossy compressors. By dissecting the sampling process, they establish a compelling link between diffusion models and autoregressive decoding, suggesting a generalized bit ordering that transcends traditional coordinate-based approaches. This research pushes the boundaries of diffusion models, establishing their utility for image generation, compression, and potential implications for other machine learning systems.

Key Topics:

Diffusion Models, Image Synthesis, Score Matching, Variational Inference, Progressive Decoding