论文标题
处理不平衡数据集的不对称对比损失
An Asymmetric Contrastive Loss for Handling Imbalanced Datasets
论文作者
论文摘要
对比学习是一种表示方法，该方法是通过将样本与其他类似样本进行对比，以使它们紧密地将其结合在一起，从而在特征空间中形成簇。学习过程通常是使用两阶段训练架构进行的，它利用对比度损失（CL）进行功能学习。对比度学习已被证明在处理不平衡数据集方面非常成功，其中某些课程的代表性过高，而另一些类的代表性不足。但是，以前的研究并未针对数据集进行不平衡的CL进行专门修改。在这项工作中，我们引入了一个不对称版本的Cl（称为ACL），以直接解决类不平衡问题。此外，我们提出了不对称的局灶性对比损失（AFCL）作为ACL和局灶性对比损失（FCL）的进一步概括。 FMNIST和ISIC 2018不平衡数据集的结果表明，AFCL能够以加权和未加权分类精度优于CL和FCL。在附录中，我们在熵上提供完整的公理处理以及完整的证明。
Contrastive learning is a representation learning method performed by contrasting a sample to other similar samples so that they are brought closely together, forming clusters in the feature space. The learning process is typically conducted using a two-stage training architecture, and it utilizes the contrastive loss (CL) for its feature learning. Contrastive learning has been shown to be quite successful in handling imbalanced datasets, in which some classes are overrepresented while some others are underrepresented. However, previous studies have not specifically modified CL for imbalanced datasets. In this work, we introduce an asymmetric version of CL, referred to as ACL, in order to directly address the problem of class imbalance. In addition, we propose the asymmetric focal contrastive loss (AFCL) as a further generalization of both ACL and focal contrastive loss (FCL). Results on the FMNIST and ISIC 2018 imbalanced datasets show that AFCL is capable of outperforming CL and FCL in terms of both weighted and unweighted classification accuracies. In the appendix, we provide a full axiomatic treatment on entropy, along with complete proofs.
