Domain Mixup

Domain mixup is a technique used in domain adaptation, aiming to improve the performance of machine learning models when training and testing data come from different distributions. Current research focuses on applying mixup strategies within various domain adaptation frameworks, including semi-supervised and unsupervised approaches, often incorporating techniques like contrastive learning and self-paced learning to enhance model robustness and generalization. These methods are proving effective across diverse applications, such as remaining useful life prediction, semantic segmentation, and object detection, by bridging the gap between source and target domains and improving accuracy even with limited labeled target data. The resulting improvements in model adaptability have significant implications for real-world applications where data scarcity or distribution shifts are common.