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Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency
Samarth Mishra · Kate Saenko · Venkatesh Saligrama

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Event URL: https://openreview.net/forum?id=sqBIm0Irju7 »

Most modern unsupervised domain adaptation (UDA) approaches are rooted in domain alignment, i.e., learning to align source and target features to learn a target domain classifier using source labels. In semi-supervised domain adaptation (SSDA), when the learner can access few target domain labels, prior approaches have followed UDA theory to use domain alignment for learning. We show that the case of SSDA is different and a good target classifier can be learned without needing explicit alignment. We use self-supervised pretraining and consistency regularization to achieve well separated target clusters, aiding in learning a low error target classifier, allowing our method to outperform recent state of the art approaches on large, challenging benchmarks like DomainNet and VisDA-17.

Author Information

Samarth Mishra (Boston University)
Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)
Kate Saenko

Kate is an AI Research Scientist at FAIR, Meta and a Full Professor of Computer Science at Boston University (currently on leave) where she leads the Computer Vision and Learning Group. Kate received a PhD in EECS from MIT and did postdoctoral training at UC Berkeley and Harvard. Her research interests are in Artificial Intelligence with a focus on out-of-distribution learning, dataset bias, domain adaptation, vision and language understanding, and other topics in deep learning. Past academic positions Consulting professor at the MIT-IBM Watson AI Lab 2019-2022. Assistant Professor, Computer Science Department at UMass Lowell Postdoctoral Researcher, International Computer Science Institute Visiting Scholar, UC Berkeley EECS Visiting Postdoctoral Fellow, SEAS, Harvard University

Venkatesh Saligrama (Boston University)

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