Abstract: Research has shown that deep neural networks (DNNs) have vulnerabilities that can lead to the misrecognition of Adversarial Examples (AEs) with specifically designed pertur-bations. Various ...
Abstract: Multi-Label Contrastive Learning (MLCL) seeks to pull samples with shared labels closer in an embedding space. However, existing methods primarily adjust attractive forces without explicitly ...
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