Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation

• URL: http://arxiv.org/abs/2210.10194v1
• Date: Tue, 18 Oct 2022 22:33:12 GMT
• Title: Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation
• Authors: Shentong Mo, Zhun Sun, Chao Li
• Abstract summary: We propose to learn Prototypical representation through Alignment, Uniformity and Correlation (PAUC) Specifically, the ordinary ProtoNCE loss is revised with: (1) an alignment loss that pulls embeddings from positive prototypes together; (2) a loss that distributes the prototypical level features uniformly; (3) a correlation loss that increases the diversity and discriminability between prototypical level features.
• Score: 24.794022951873156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive self-supervised learning (CSL) with a prototypical regularization has been introduced in learning meaningful representations for downstream tasks that require strong semantic information. However, to optimize CSL with a loss that performs the prototypical regularization aggressively, e.g., the ProtoNCE loss, might cause the "coagulation" of examples in the embedding space. That is, the intra-prototype diversity of samples collapses to trivial solutions for their prototype being well-separated from others. Motivated by previous works, we propose to mitigate this phenomenon by learning Prototypical representation through Alignment, Uniformity and Correlation (PAUC). Specifically, the ordinary ProtoNCE loss is revised with: (1) an alignment loss that pulls embeddings from positive prototypes together; (2) a uniformity loss that distributes the prototypical level features uniformly; (3) a correlation loss that increases the diversity and discriminability between prototypical level features. We conduct extensive experiments on various benchmarks where the results demonstrate the effectiveness of our method in improving the quality of prototypical contrastive representations. Particularly, in the classification down-stream tasks with linear probes, our proposed method outperforms the state-of-the-art instance-wise and prototypical contrastive learning methods on the ImageNet-100 dataset by 2.96% and the ImageNet-1K dataset by 2.46% under the same settings of batch size and epochs.

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