Deconstructing Denoising Diffusion Models for Self-Supervised Learning
Xinlei Chen Zhuang Liu Saining Xie Kaiming He
In this study, we examine the representation learning abilities of Denoising Diffusion Models (DDM) that were originally purposed for image generation. Our philosophy is to deconstruct a DDM, gradually transforming it into a classical Denoising Autoencoder (DAE). This deconstructive process allows us to explore how various components of modern DDMs influence self-supervised representation learning. We observe that only a very few modern components are critical for learning good representations, while many others are nonessential. Our study ultimately arrives at an approach that is highly simplified and to a large extent resembles a classical DAE. We hope our study will rekindle interest in a family of classical methods within the realm of modern self-supervised learning.
denoising diffusion models, denoising autoencoder, self-supervised learning
deconstructing a modern denoising diffusion model to a classical denoising autoencoder for self-supervised learning