Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation
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Abstract (arXiv)
Understanding unsupervised domain adaptation has been an important task that has been well explored. However, the wide variety of methods have not analyzed the role of a classifier's performance in detail. In this paper, we thoroughly examine the role of a classifier in terms of matching source and target distributions. We specifically investigate the classifier ability by matching a) the distribution of features, b) probabilistic uncertainty for samples and c) certainty activation mappings. Our analysis suggests that using these three distributions does result in a consistently improved performance on all the datasets. Our work thus extends present knowledge on the role of the various distributions obtained from the classifier towards solving unsupervised domain adaptation.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 35 of its 50 references that are in the map. First posted on arXiv 1 Jul 2021.
Builds on
35 of 50 references.
References · 35
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23 more
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.
2 papers.