Using Diffusion Models to Visualize What Self-Supervised Neural Networks Actually Learn
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[Submitted on 16 Dec 2021 (v1), last revised 16 Aug 2022 (this version, v2)]
Summary
This paper introduces the use of Representation Conditional Diffusion Models (RCDM) to visualize what self-supervised learning (SSL) models actually learn. The authors demonstrate that RCDMs can generate high-quality samples faithful to the representations they condition on. Using this visualization technique, they make four key findings: (1) SSL backbone representations are NOT invariant to data augmentations they were trained with, debunking a common misconception; (2) SSL post-projector embeddings do appear invariant to augmentations and other symmetries; (3) SSL representations are more robust to small adversarial perturbations than supervised representations; and (4) SSL representations have an inherent structure that enables image manipulation through RCDM visualization.
Source
bskyUsing Diffusion Models to Visualize What Self-Supervised Neural Networks Actually Learnarxiv.orgKey quotes
· 5 pulledDiscovering what is learned by neural networks remains a challenge.
In self-supervised learning, classification is the most common task used to evaluate how good a representation is.
SSL (backbone) representation are not invariant to the data augmentations they were trained with -- thus debunking an often restated but mistaken belief.
SSL post-projector embeddings appear indeed invariant to these data augmentation, along with many other data symmetries.
SSL-trained representations exhibit an inherent structure that can be explored thanks to RCDM visualization and enables image manipulation.
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