传统对比学习算法进行实例判别时容易引入虚假负样本,导致模型收敛于次优解,影响下游任务性能.为此,提出一种基于关系一致性的多分支对比学习算法.该算法在分支网络中挖掘近邻集,提供语义一致的正样本,避免产生假的负样本.结合数据增强的多分支网络,最小化KL散度拉近语义一致性的正样本推开负样本,提升网络的特征表达能力.不同分支的温度控制输出分布的平滑性,保证特征表示的真实可靠性.最后在5个数据集上测试所提算法,并与其他先进方法进行对比,均获得令人满意的结果.
Traditional contrastive learning algorithms have relied on instance discrimination, which is easy to introduce false negative samples, resulting in algorithm converges to suboptimal solutions and degenerating the performance of downstream tasks. To address this issue, this paper proposes a multi-branch contrastive learning algorithm based on relationship consistency. This algorithm incorporates a nearest neighbor set into the branch network, providing positive samples with their semantic consistency and avoiding false negative samples. Combined with multi-branch training using data augmentation, it minimizes KL divergence to pull positive samples with semantic consistency together and push away negative samples, which enhances the representation ability of network. The temperature parameters in different branches control the smoothness of the sharpen distribution, ensuring the reliability of feature representations. Finally, the method is evaluated on five public datasets and compared with state-of-the-art contrastive learning methods, all achieving satisfactory results.
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