Contrastive learning (CL) is effective in learning data representations without label supervision, where the encoder needs to contrast each positive sample over multiple negative samples via a one-vs-many softmax cross-entropy loss. However, conventional CL is sensitive to how many negative samples are included and how they are selected. Proposed in this paper is a doubly CL strategy that contrasts positive samples and negative ones within themselves separately. We realize this strategy with contrastive attraction and contrastive repulsion (CACR) makes the query not only exert a greater force to attract more distant positive samples but also do so to repel closer negative samples. Theoretical analysis reveals the connection between CACR and CL from the perspectives of both positive attraction and negative repulsion and shows the benefits in both efficiency and robustness brought by separately contrasting within the sampled positive and negative pairs. Extensive large-scale experiments on standard vision tasks show that CACR not only consistently outperforms existing CL methods on benchmark datasets in representation learning, but also provides interpretable contrastive weights, demonstrating the efficacy of the proposed doubly contrastive strategy.
تحميل البحث