Mishra, SamarthSaenko, KateSaligrama, Venkatesh2022-03-282022-03-282021S. Mishra, K. Saenko, V. Saligrama. 2021. "Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency." https://arxiv.org/abs/2101.12727.https://hdl.handle.net/2144/44089Visual domain adaptation involves learning to classify images from a target visual domain using labels available in a different source domain. A range of prior work uses adversarial domain alignment to try and learn a domain invariant feature space, where a good source classifier can perform well on target data. This however, can lead to errors where class A features in the target domain get aligned to class B features in source. We show that in the presence of a few target labels, simple techniques like selfsupervision (via rotation prediction) and consistency regularization can be effective without any adversarial alignment to learn a good target classifier. Our Pretraining and Consistency (PAC) approach, can achieve state of the art accuracy on this semi-supervised domain adaptation task, surpassing multiple adversarial domain alignment methods, across multiple datasets. Notably, it outperforms all recent approaches by 3-5% on the large and challenging DomainNet benchmark, showing the strength of these simple techniques in fixing errors made by adversarial alignmenten-USArticle604418