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Date
2022Type
- Conference Paper
ETH Bibliography
yes
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Abstract
We study the problem of robust domain adaptation in the context of unavailable target labels and source data. The considered robustness is against adversarial perturbations. This paper aims at answering the question of finding the right strategy to make the target model robust and accurate in the setting of unsupervised domain adaptation without source data. The major findings of this paper are: (i) robust source models can be transferred robustly to the target; (ii) robust domain adaptation can greatly benefit from non-robust pseudo-labels and the pair-wise contrastive loss. The proposed method of using non-robust pseudo-labels performs surprisingly well on both clean and adversarial samples, for the task of image classification. We show a consistent performance improvement of over 10% in accuracy against the tested baselines on four benchmark datasets. Our source code will be made publicly available. Show more
Publication status
publishedExternal links
Book title
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)Pages / Article No.
Publisher
IEEEEvent
Subject
Deep learning -> adversary learning; Adversarial attack and defense methods datasets; Evaluation and comparison of vision algorithms; Transfer; Few-shot; Semi- and un- supervised learningOrganisational unit
03514 - Van Gool, Luc / Van Gool, Luc
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ETH Bibliography
yes
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