Incremental False Negative Detection for Contrastive Learning

Tsai-Shien Chen, Wei-Chih Hung, Hung-Yu Tseng, Shao-Yi Chien, Ming-Hsuan Yang

Self-supervised learning has recently shown great potential in vision tasks via contrastive learning, which aims to discriminate each image, or instance, in the dataset. However, such instance-level learning ignores the semantic relationship between instances and repels the anchor equally from the semantically similar samples, termed as false negatives. In this work, we first empirically highlight that the unfavorable effect from false negatives is more significant for the datasets containing images with more semantic concepts. To address the issue, we introduce a novel incremental false negative detection for self-supervised contrastive learning. Following the training process, when the encoder is gradually better-trained and the embedding space becomes more semantically structural, our method incrementally detects more reliable false negatives. Subsequently, during contrastive learning, we discuss two strategies to explicitly remove the detected false negatives. Extensive experiments show that our proposed method outperforms other self-supervised contrastive learning frameworks on multiple benchmarks within a limited compute.

Knowledge Graph

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