Diffusion probabilistic models (DPMs) have achieved remarkable quality in image generation that rivals GANs'. But unlike GANs, DPMs use a set of latent variables that lack semantic meaning and cannot serve as a useful representation for other tasks. This paper explores the possibility of using DPMs for representation learning and seeks to extract a meaningful and decodable representation of an input image via autoencoding. Our key idea is to use a learnable encoder for discovering the high-level semantics, and a DPM as the decoder for modeling the remaining stochastic variations. Our method can encode any image into a two-part latent code, where the first part is semantically meaningful and linear, and the second part captures stochastic details, allowing near-exact reconstruction. This capability enables challenging applications that currently foil GAN-based methods, such as attribute manipulation on real images. We also show that this two-level encoding improves denoising efficiency and naturally facil itates various downstream tasks including few-shot conditional sampling.

本研究探索使用可扩散概率模型作为自编码器的解码器，让输入图像生成一个可以被意义化和解码的编码向量，该编码向量分为两部分：第一部分是含有语义的线性编码，而第二部分是建模随机变量的编码，提升了图像生成的质量并且在其他应用中可以用来做属性调整和去噪等任务。

扩散自编码器：走向有意义和可解码的表征