通过锚-正样本对熵加权置信度整合提升半监督对比学习
Improving Semi-Supervised Contrastive Learning via Entropy-Weighted Confidence Integration of Anchor-Positive Pairs
AI总结：
本文提出基于熵加权置信度整合的半监督对比学习方法，通过提升伪标签分配的置信度来增强分类准确率和学习稳定性。
摘要：
传统半监督对比学习方法仅将预测类概率最高且超过预设阈值的样本分配伪标签，然后使用这些选定样本进行监督对比学习。本研究提出了一种新的损失函数，该函数基于预测概率分布的熵估计每个样本的置信度，并应用基于置信度的自适应加权。这种方法使之前被排除在训练之外的样本也能获得伪标签分配，并以更系统的方式考虑锚样本和正样本的置信度进行对比学习。实验结果表明，所提出的方法提高了分类准确率，并在低标签条件下实现了更稳定的学習性能。
英文摘要：
Conventional semi-supervised contrastive learning methods assign pseudo-labels only to samples whose highest predicted class probability exceeds a predefined threshold, and then perform supervised contrastive learning using those selected samples. In this study, we propose a novel loss function that estimates the confidence of each sample based on the entropy of its predicted probability distribution and applies confidence-based adaptive weighting. This approach enables pseudo-label assignment even to samples that were previously excluded from training and facilitates contrastive learning that accounts for the confidence of both anchor and positive samples in a more principled manner. Experimental results demonstrate that the proposed method improves classification accuracy and achieves more stable learning performance even under low-label conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
