Multi Target Domain Adaptation

Multi-target domain adaptation (MTDA) aims to train a single model capable of performing well across multiple unlabeled target domains, given data from a single labeled source domain. Current research focuses on addressing challenges like open-set scenarios (where target domains contain unseen classes), limited data access, and efficient model adaptation, employing techniques such as prompt learning, model merging, and knowledge distillation with various neural network architectures. These advancements are significant for improving the generalizability and robustness of machine learning models in real-world applications where data distributions vary widely, impacting fields like medical image analysis, autonomous driving, and remote sensing.