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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 09410643 |
| DOI: | 10.1007/s00521-025-11236-z |
