Source Domain

Source domain research focuses on adapting models trained on a labeled source dataset to perform well on a different, often unlabeled, target dataset. Current efforts concentrate on techniques like pseudo-labeling, contrastive learning, and optimal transport, often employing deep neural networks (including vision-language models and various convolutional architectures) to bridge the distributional gap between domains. This work is crucial for addressing data scarcity and privacy concerns in various applications, such as medical image analysis, recommendation systems, and natural language processing, where labeled data from the target domain may be limited or unavailable. The ultimate goal is to improve the generalizability and robustness of machine learning models across diverse data distributions.