Failure-Proof Non-Contrastive Self-Supervised Learning
Keywords: Self-Supervised Learning, Representation Learning, Deep Learning
TL;DR: This paper provides the first non-contrastive solution that is guaranteed to avoid known collapses. Learnt features are both decorrelated and clustered so as to guarantee improved generalization to downstream tasks.
Abstract: We identify sufficient conditions to avoid known failure modes, including representation, dimensional, cluster and intracluster collapses, occurring in non-contrastive self-supervised learning. Based on these findings, we propose a principled design for the projector and loss function. We theoretically demonstrate that this design introduces an inductive bias that promotes learning representations that are both decorrelated and clustered without explicit enforcing these properties and leading to improved generalization. To the best of our knowledge, this is the first solution that achieves robust training with respect to these failure modes while guaranteeing enhanced generalization performance in downstream tasks. We validate our theoretical findings on image datasets including SVHN, CIFAR10, CIFAR100 and ImageNet-100, and show that our solution, dubbed FALCON, outperforms existing feature decorrelation and cluster-based self-supervised learning methods in terms of generalization to clustering and linear classification tasks.
Supplementary Material: pdf
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 11164
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