Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring Adversarially Robust Training for Unsupervised Domain
  Adaptation   Unsupervised Domain Adaptation (UDA) methods aim to transfer knowledge from a
labeled source domain to an unlabeled target domain. UDA has been extensively
studied in the computer vision literature. Deep networks have been shown to be
vulnerable to adversarial attacks. However, very little focus is devoted to
improving the adversarial robustness of deep UDA models, causing serious
concerns about model reliability. Adversarial Training (AT) has been considered
to be the most successful adversarial defense approach. Nevertheless,
conventional AT requires ground-truth labels to generate adversarial examples
and train models, which limits its effectiveness in the unlabeled target
domain. In this paper, we aim to explore AT to robustify UDA models: How to
enhance the unlabeled data robustness via AT while learning domain-invariant
features for UDA? To answer this, we provide a systematic study into multiple
AT variants that potentially apply to UDA. Moreover, we propose a novel
Adversarially Robust Training method for UDA accordingly, referred to as
ARTUDA. Extensive experiments on multiple attacks and benchmarks show that
ARTUDA consistently improves the adversarial robustness of UDA models.

3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
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The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc