Title: Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application Show Chinese title

Title: 对比学习与领域适应的桥梁：理论视角与实践应用

Abstract: This work studies the relationship between Contrastive Learning and Domain Adaptation from a theoretical perspective. The two standard contrastive losses, NT-Xent loss (Self-supervised) and Supervised Contrastive loss, are related to the Class-wise Mean Maximum Discrepancy (CMMD), a dissimilarity measure widely used for Domain Adaptation. Our work shows that minimizing the contrastive losses decreases the CMMD and simultaneously improves class-separability, laying the theoretical groundwork for the use of Contrastive Learning in the context of Domain Adaptation. Due to the relevance of Domain Adaptation in medical imaging, we focused the experiments on mammography images. Extensive experiments on three mammography datasets - synthetic patches, clinical (real) patches, and clinical (real) images - show improved Domain Adaptation, class-separability, and classification performance, when minimizing the Supervised Contrastive loss. Show Chinese abstract

Abstract: 本研究从理论角度探讨了对比学习与领域自适应之间的关系。两种标准的对比损失函数——NT-Xent 损失（自监督）和有监督对比损失——与广泛用于领域自适应的类别均值最大差异度量（CMMD）相关。我们的研究表明，最小化对比损失函数可以减小 CMMD 并同时提高类间可分性，在领域自适应背景下为对比学习的应用奠定了理论基础。由于领域自适应在医学影像中的重要性，我们将实验重点放在乳腺 X 光片图像上。在三个乳腺 X 光片数据集（合成块、临床（真实）块和临床（真实）图像）上的大量实验表明，当最小化有监督对比损失时，领域自适应、类间可分性和分类性能均得到提升。