Enhancing self-supervised visual representation learning through adversarially generated examples.
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| Title: | Enhancing self-supervised visual representation learning through adversarially generated examples. |
|---|---|
| Authors: | Kang, Mintae1 (AUTHOR) kmt876@kaist.ac.kr, Kim, Junmo1 (AUTHOR) junmo.kim@kaist.ac.kr |
| Source: | Neural Computing & Applications. Jul2025, Vol. 37 Issue 19, p14613-14634. 22p. |
| Subjects: | Machine learning, Visual learning, Generalization, Supervised learning, Classification |
| Abstract: | Self-supervised learning has emerged as a powerful paradigm for leveraging unlabeled data to learn rich feature representations. However, the efficacy of self-supervised models is often limited by the degree and complexity of the augmentations used during training. In this work, we propose a novel framework that enhances self-supervised learning by incorporating a generative network designed to produce adversarial examples that challenge the learning process. By integrating adversarially generated data, our method extends three well-known self-supervised architectures---SimCLR, BYOL, and SimSiam---and improves their generalization and robustness. We evaluate our approach on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets, demonstrating consistent improvements in classification accuracy over baseline models. Notably, our proposed method outperforms standard self-supervised learning techniques, achieving significant gains in top-1 accuracy across all datasets and training epochs. This substantiates our hypothesis that adversarial examples can significantly contribute to the feature learning capabilities of self-supervised models. Furthermore, our findings suggest that the integration of generative networks can serve as a catalyst for the development of more advanced self-supervised learning algorithms. This study lays the groundwork for future research exploring the potential of adversarial training in self-supervised learning and its applications across diverse domains. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 186243343 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhancing self-supervised visual representation learning through adversarially generated examples. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kang%2C+Mintae%22">Kang, Mintae</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kmt876@kaist.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Kim%2C+Junmo%22">Kim, Junmo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> junmo.kim@kaist.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Jul2025, Vol. 37 Issue 19, p14613-14634. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+learning%22">Visual learning</searchLink><br /><searchLink fieldCode="DE" term="%22Generalization%22">Generalization</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Self-supervised learning has emerged as a powerful paradigm for leveraging unlabeled data to learn rich feature representations. However, the efficacy of self-supervised models is often limited by the degree and complexity of the augmentations used during training. In this work, we propose a novel framework that enhances self-supervised learning by incorporating a generative network designed to produce adversarial examples that challenge the learning process. By integrating adversarially generated data, our method extends three well-known self-supervised architectures---SimCLR, BYOL, and SimSiam---and improves their generalization and robustness. We evaluate our approach on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets, demonstrating consistent improvements in classification accuracy over baseline models. Notably, our proposed method outperforms standard self-supervised learning techniques, achieving significant gains in top-1 accuracy across all datasets and training epochs. This substantiates our hypothesis that adversarial examples can significantly contribute to the feature learning capabilities of self-supervised models. Furthermore, our findings suggest that the integration of generative networks can serve as a catalyst for the development of more advanced self-supervised learning algorithms. This study lays the groundwork for future research exploring the potential of adversarial training in self-supervised learning and its applications across diverse domains. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-025-11236-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 14613 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Visual learning Type: general – SubjectFull: Generalization Type: general – SubjectFull: Supervised learning Type: general – SubjectFull: Classification Type: general Titles: – TitleFull: Enhancing self-supervised visual representation learning through adversarially generated examples. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kang, Mintae – PersonEntity: Name: NameFull: Kim, Junmo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 37 – Type: issue Value: 19 Titles: – TitleFull: Neural Computing & Applications Type: main |
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