桥接对比学习与领域自适应：理论视角与实际应用
Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application
AI总结：
本研究从理论视角揭示了对比学习与领域自适应的联系，证明最小化对比损失能降低CMMD并提升类别可分性，并在乳腺X光数据集上验证了其改善领域自适应和分类性能的有效性。
摘要：
本研究从理论视角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，即NT-Xent loss（自监督）和Supervised Contrastive loss，与类别均值最大差异（CMMD，一种广泛用于领域自适应的差异度量）相关联。我们的工作表明，最小化对比损失能降低CMMD并同时提升类别可分性，为在领域自适应背景下使用对比学习奠定了理论基础。由于领域自适应在医学成像中的相关性，我们将实验重点放在乳腺X光图像上。在三个乳腺X光数据集（合成图像块、临床（真实）图像块和临床（真实）图像）上的大量实验表明，最小化Supervised Contrastive loss能改善领域自适应、类别可分性和分类性能。
英文摘要：
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.
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。
