Distributionally Robust Learning for Unsupervised Domain AdaptationDownload PDF

28 Sept 2020 (modified: 05 May 2023)ICLR 2021 Conference Blind SubmissionReaders: Everyone
Reviewed Version (pdf): https://openreview.net/references/pdf?id=Sx80ZbWyew
Keywords: Distributionally Robust Learning, Domain Adaptation, Self-training, Density Ratio Estimation
Abstract: We propose a distributionally robust learning (DRL) method for unsupervised domain adaptation (UDA) that scales to modern computer-vision benchmarks. DRL can be naturally formulated as a competitive two-player game between a predictor and an adversary that is allowed to corrupt the labels, subject to certain constraints, and reduces to incorporating a density ratio between the source and target domains (under the standard log loss). This formulation motivates the use of two neural networks that are jointly trained --- a discriminative network between the source and target domains for density-ratio estimation, in addition to the standard classification network. The use of a density ratio in DRL prevents the model from being overconfident on target inputs far away from the source domain. Thus, DRL provides conservative confidence estimation in the target domain, even when the target labels are not available. This conservatism motivates the use of DRL in self-training for sample selection, and we term the approach distributionally robust self-training (DRST). In our experiments, DRST generates more calibrated probabilities and achieves state-of-the-art self-training accuracy on benchmark datasets. We demonstrate that DRST captures shape features more effectively, and reduces the extent of distributional shift during self-training.
One-sentence Summary: We propose a distributionally robust method for unsupervised domain adaptation that gives conservative uncertainties and SOTA accuracy.
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