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DC Field | Value | Language |
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dc.contributor.author | Wang, C | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Liu, Q | - |
dc.contributor.author | Dong, H | - |
dc.contributor.author | Sheng, W | - |
dc.date.accessioned | 2024-03-19T15:11:56Z | - |
dc.date.available | 2024-03-19T15:11:56Z | - |
dc.date.issued | 2024-01-09 | - |
dc.identifier | ORCiD: Chuang Wang https://orcid.org/0000-0001-8938-9312 | - |
dc.identifier | ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401 | - |
dc.identifier | ORCiD: Qinyuan Liu https://orcid.org/0000-0002-0170-3651 | - |
dc.identifier | ORCiD: Hongli Dong https://orcid.org/0000-0001-8531-6757 | - |
dc.identifier.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. | en_US |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28578 | - |
dc.description.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. | en_US |
dc.description.sponsorship | 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61933007, U21A2019 and 62222312); Hainan Province Science and Technology Special Fund of China (Grant Number: ZDYF2022SHFZ105); 10.13039/501100012226-Fundamental Research Funds for the Central Universities R&D Program of Zhejiang Province of China (Grant Number: 2023C01022); National Key Research and Development Program of China (Grant Number: YS2022YFB4500205); Shanghai Science and Technology Innovation Action Plan Project (Grant Number: 22511100700); Alexander von Humboldt Foundation of Germany. | en_US |
dc.format.extent | 1 - 11 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2023 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. See https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ for more information | - |
dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.subject | attack–defense strategy | en_US |
dc.subject | domain adaptation (DA) | en_US |
dc.subject | domain generalization (DG) | en_US |
dc.subject | support sample | en_US |
dc.subject | transfer learning (TL) | en_US |
dc.title | Support-Sample-Assisted Domain Generalization via Attacks and Defenses: Concepts, Algorithms, and Applications to Pipeline Fault Diagnosis | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/TII.2023.3337364 | - |
dc.relation.isPartOf | IEEE Transactions on Industrial Informatics | - |
pubs.issue | early access | - |
pubs.publication-status | Published | - |
pubs.volume | 0 | - |
dc.identifier.eissn | 1941-0050 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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