Single Target Domain Adaptation

Single-target domain adaptation (STDA) aims to improve the performance of machine learning models on a target dataset with a different distribution than the source dataset used for training, using only unlabeled target data. Current research focuses on developing robust algorithms, often employing teacher-student frameworks, adversarial training, and ensemble methods, to effectively transfer knowledge across domains and mitigate the impact of differing data distributions. This is crucial for real-world applications where labeled data is scarce in the target domain, such as in autonomous driving or medical image analysis, enabling more reliable and generalizable models. The extension of these techniques to multi-target scenarios, where adaptation is needed across multiple target domains simultaneously, is also a significant area of ongoing investigation.