Unsupervised Domain Adaptation by Uncertain Feature Alignment

• URL: http://arxiv.org/abs/2009.06483v1
• Date: Mon, 14 Sep 2020 14:42:41 GMT
• Title: Unsupervised Domain Adaptation by Uncertain Feature Alignment
• Authors: Tobias Ringwald, Rainer Stiefelhagen
• Abstract summary: 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.
• Score: 29.402619219254074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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