{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T09:10:02Z","timestamp":1751015402557,"version":"3.41.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Korea Advanced Institute of Science and Technology"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>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.<\/jats:p>","DOI":"10.1007\/s00521-025-11236-z","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T17:57:07Z","timestamp":1747245427000},"page":"14613-14634","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing self-supervised visual representation learning through adversarially generated examples"],"prefix":"10.1007","volume":"37","author":[{"given":"Mintae","family":"Kang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7174-7932","authenticated-orcid":false,"given":"Junmo","family":"Kim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"11236_CR1","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. 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