IT-RUDA: Information Theory Assisted Robust Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2210.12947v1
• Date: Mon, 24 Oct 2022 04:33:52 GMT
• Title: IT-RUDA: Information Theory Assisted Robust Unsupervised Domain Adaptation
• Authors: Shima Rashidi, Ruwan Tennakoon, Aref Miri Rekavandi, Papangkorn Jessadatavornwong, Amanda Freis, Garret Huff, Mark Easton, Adrian Mouritz, Reza Hoseinnezhad, Alireza Bab-Hadiashar
• Abstract summary: Distribution shift between train (source) and test (target) datasets is a common problem encountered in machine learning applications. UDA technique carries out knowledge transfer from a label-rich source domain to an unlabeled target domain. Outliers that exist in either source or target datasets can introduce additional challenges when using UDA in practice.
• Score: 7.225445443960775
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Distribution shift between train (source) and test (target) datasets is a common problem encountered in machine learning applications. One approach to resolve this issue is to use the Unsupervised Domain Adaptation (UDA) technique that carries out knowledge transfer from a label-rich source domain to an unlabeled target domain. Outliers that exist in either source or target datasets can introduce additional challenges when using UDA in practice. In this paper, $\alpha$-divergence is used as a measure to minimize the discrepancy between the source and target distributions while inheriting robustness, adjustable with a single parameter $\alpha$, as the prominent feature of this measure. Here, it is shown that the other well-known divergence-based UDA techniques can be derived as special cases of the proposed method. Furthermore, a theoretical upper bound is derived for the loss in the target domain in terms of the source loss and the initial $\alpha$-divergence between the two domains. The robustness of the proposed method is validated through testing on several benchmarked datasets in open-set and partial UDA setups where extra classes existing in target and source datasets are considered as outliers.

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