The content delves into the importance of uniformity in self-supervised learning, highlighting the limitations of the current metric and proposing a new one. It discusses theoretical analysis, empirical evidence, and experiments to support the efficacy of the new metric.
Uniformity is crucial in self-supervised learning for assessing learned representations. The existing uniformity metric lacks sensitivity to dimensional collapse, prompting the introduction of a novel metric that overcomes this limitation. The proposed metric consistently enhances performance in downstream tasks when integrated into established self-supervised methods.
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by Xianghong Fa... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00642.pdfDeeper Inquiries