论文标题
域的概括与领域增强受监督的对比学习（学生摘要）
Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)
论文作者
论文摘要
当训练和目标域的分布之间存在不匹配时，域概括（DG）方法解决了域移位的问题。数据增强方法已成为DG的有希望的替代方法。但是，仅数据增强不足以达到较低的概括错误。该项目提出了一种新方法，该方法结合了数据增强和域距离最小化，以解决与数据增强相关的问题，并在现有框架下为学习绩效提供保证。从经验上讲，我们的方法在DG基准上优于基线结果。
Domain generalisation (DG) methods address the problem of domain shift, when there is a mismatch between the distributions of training and target domains. Data augmentation approaches have emerged as a promising alternative for DG. However, data augmentation alone is not sufficient to achieve lower generalisation errors. This project proposes a new method that combines data augmentation and domain distance minimisation to address the problems associated with data augmentation and provide a guarantee on the learning performance, under an existing framework. Empirically, our method outperforms baseline results on DG benchmarks.
