Deconstructing Denoising Diffusion Models for Self-Supervised Learning
Keywords: denoising diffusion models, denoising autoencoder, self-supervised learning
TL;DR: deconstructing a modern denoising diffusion model to a classical denoising autoencoder for self-supervised learning
Abstract: 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.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 7485
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