AI RESEARCH
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
arXiv CS.LG
•
ArXi:2606.04665v1 Announce Type: new Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain. However, algorithm comparison is cumbersome in Deep UDA due to the absence of accurate and standardized model selection method, posing an obstacle to further advances in the field. Existing model selection methods for Deep UDA are either highly biased, restricted, unstable, or even controversial (requiring labeled target data