TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification

• URL: http://arxiv.org/abs/2505.06580v1
• Date: Sat, 10 May 2025 09:43:04 GMT
• Title: TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification
• Authors: Dongyoon Yang, Jihu Lee, Yongdai Kim,
• Abstract summary: TAROT is designed to enhance both domain adaptability and robustness.<n>It achieves superior performance on the challenging DomainNet dataset.<n>Results highlight the broader applicability of our approach in real-world domain adaptation scenarios.
• Score: 2.3718468096686802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust domain adaptation against adversarial attacks is a critical research area that aims to develop models capable of maintaining consistent performance across diverse and challenging domains. In this paper, we derive a new generalization bound for robust risk on the target domain using a novel divergence measure specifically designed for robust domain adaptation. Building upon this, we propose a new algorithm named TAROT, which is designed to enhance both domain adaptability and robustness. Through extensive experiments, TAROT not only surpasses state-of-the-art methods in accuracy and robustness but also significantly enhances domain generalization and scalability by effectively learning domain-invariant features. In particular, TAROT achieves superior performance on the challenging DomainNet dataset, demonstrating its ability to learn domain-invariant representations that generalize well across different domains, including unseen ones. These results highlight the broader applicability of our approach in real-world domain adaptation scenarios.

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