Unsupervised Domain Adaptation by Uncertain Feature Alignment

Tobias Ringwald; Rainer Stiefelhagen

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arXiv · 2009.06483

Unsupervised Domain Adaptation by Uncertain Feature Alignment

Tobias Ringwald·Rainer Stiefelhagen

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Abstract

Unsupervised domain adaptation (UDA) deals with the adaptation of models from a given source domain with labeled data to an unlabeled target domain. In this paper, we utilize the inherent prediction uncertainty of a model to accomplish the domain adaptation task. The uncertainty is measured by Monte-Carlo dropout and used for our proposed Uncertainty-based Filtering and Feature Alignment (UFAL) that combines an Uncertain Feature Loss (UFL) function and an Uncertainty-Based Filtering (UBF) approach for alignment of features in Euclidean space. Our method surpasses recently proposed architectures and achieves state-of-the-art results on multiple challenging datasets. Code is available on the project website.

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BibTeX RIS

Tobias Ringwald, Rainer Stiefelhagen. 2020-09-14. Unsupervised Domain Adaptation by Uncertain Feature Alignment. https://arxiv.org/abs/2009.06483

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