Incremental False Negative Detection for Contrastive Learning
This addresses a specific issue in self-supervised learning for vision tasks, offering an incremental improvement to enhance semantic relationships in large-scale datasets.
The paper tackles the problem of false negatives in contrastive learning, which repel semantically similar samples, by proposing a framework that incrementally detects and removes these false negatives, leading to outperformance of other self-supervised methods on multiple benchmarks in limited resource setups.
Self-supervised learning has recently shown great potential in vision tasks through contrastive learning, which aims to discriminate each image, or instance, in the dataset. However, such instance-level learning ignores the semantic relationship among instances and sometimes undesirably repels the anchor from the semantically similar samples, termed as "false negatives". In this work, we show that the unfavorable effect from false negatives is more significant for the large-scale datasets with more semantic concepts. To address the issue, we propose a novel self-supervised contrastive learning framework that incrementally detects and explicitly removes the false negative samples. Specifically, following the training process, our method dynamically detects increasing high-quality false negatives considering that the encoder gradually improves and the embedding space becomes more semantically structural. Next, we discuss two strategies to explicitly remove the detected false negatives during contrastive learning. Extensive experiments show that our framework outperforms other self-supervised contrastive learning methods on multiple benchmarks in a limited resource setup.