Abstract: Highlights•A contrastive learning framework supervised by complementary labels.•Positive and negative selection strategy to draw reliable samples for contrastive learning.•Perspective of mining negative semantic information in complementary labels.•Experimental attempt on fine-grained and large-scale real-world datasets in CLL.
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