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Title: | Support-Sample-Assisted Domain Generalization via Attacks and Defenses: Concepts, Algorithms, and Applications to Pipeline Fault Diagnosis |
Authors: | Wang, C Wang, Z Liu, Q Dong, H Sheng, W |
Keywords: | attack–defense strategy;domain adaptation (DA);domain generalization (DG);support sample;transfer learning (TL) |
Issue Date: | 9-Jan-2024 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Wang, C. et al (2024) 'Support-Sample-Assisted Domain Generalization via Attacks and Defenses: Concepts, Algorithms, and Applications to Pipeline Fault Diagnosis', IEEE Transactions on Industrial Informatics, 0 (early access), pp. 1 - 11. doi: 10.1109/TII.2023.3337364. |
Abstract: | This article is concerned with domain generalization (DG), a practical yet challenging scenario in transfer learning where the target data are not available in advance. The key insight of DG is focused on learning a robust model that can generalize to the unseen domain by leveraging knowledge from the source domain. To this end, we propose a novel algorithm known as support-sample-assisted Adversarial Attacks (SSAA) for DG. In the SSAA algorithm, an attack–defense strategy is deployed to enhance the target model's generalizability and transferability. This strategy includes a nontargeted attack stage, during which attack samples are generated to form pseudotarget domains with near-realistic covariate shifts. Subsequently, in the model defense stage, a biclassifier structure is used to distinguish support samples from the generated attack samples. These support samples form a new decision boundary encompassing all unseen samples, prompting an extension of the existing decision boundary to meet these samples. Experimental results on cross-domain fault diagnosis tasks suggest that SSAA outperforms current state-of-the-art DG methods, indicating a promising avenue for further DG development. |
URI: | https://bura.brunel.ac.uk/handle/2438/28578 |
DOI: | https://doi.org/10.1109/TII.2023.3337364 |
ISSN: | 1551-3203 |
Other Identifiers: | ORCiD: Chuang Wang https://orcid.org/0000-0001-8938-9312 ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401 ORCiD: Qinyuan Liu https://orcid.org/0000-0002-0170-3651 ORCiD: Hongli Dong https://orcid.org/0000-0001-8531-6757 |
Appears in Collections: | Dept of Computer Science Research Papers |
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