Domain Adaptation Using Pseudo Labels
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Abstract (arXiv)
In the absence of labeled target data, unsupervised domain adaptation approaches seek to align the marginal distributions of the source and target domains in order to train a classifier for the target. Unsupervised domain alignment procedures are category-agnostic and end up misaligning the categories. We address this problem by deploying a pretrained network to determine accurate labels for the target domain using a multi-stage pseudo-label refinement procedure. The filters are based on the confidence, distance (conformity), and consistency of the pseudo labels. Our results on multiple datasets demonstrate the effectiveness of our simple procedure in comparison with complex state-of-the-art techniques.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 35 of its 59 references that are in the map. First posted on arXiv 9 Feb 2024.
Builds on
35 of 59 references.
References · 35
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