Towards Robust Artificial Intelligence: Self-Supervised Learning Approach for Out-of-Distribution Detection
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912647044136960 |
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| author | Salhab, Wissam Ameyed, Darine Mcheick, Hamid Jaafar, Fehmi |
| author_facet | Salhab, Wissam Ameyed, Darine Mcheick, Hamid Jaafar, Fehmi |
| contents | Robustness in AI systems refers to their ability to maintain reliable and accurate performance under various conditions, including out-of-distribution (OOD) samples, adversarial attacks, and environmental changes. This is crucial in safety-critical systems, such as autonomous vehicles, transportation, or healthcare, where malfunctions could have severe consequences. This paper proposes an approach to improve OOD detection without the need of labeled data, thereby increasing the AI systems' robustness. The proposed approach leverages the principles of self-supervised learning, allowing the model to learn useful representations from unlabeled data. Combined with graph-theoretical techniques, this enables the more efficient identification and categorization of OOD samples. Compared to existing state-of-the-art methods, this approach achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) = 0.99. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12713 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Robust Artificial Intelligence: Self-Supervised Learning Approach for Out-of-Distribution Detection Salhab, Wissam Ameyed, Darine Mcheick, Hamid Jaafar, Fehmi Artificial Intelligence Robustness in AI systems refers to their ability to maintain reliable and accurate performance under various conditions, including out-of-distribution (OOD) samples, adversarial attacks, and environmental changes. This is crucial in safety-critical systems, such as autonomous vehicles, transportation, or healthcare, where malfunctions could have severe consequences. This paper proposes an approach to improve OOD detection without the need of labeled data, thereby increasing the AI systems' robustness. The proposed approach leverages the principles of self-supervised learning, allowing the model to learn useful representations from unlabeled data. Combined with graph-theoretical techniques, this enables the more efficient identification and categorization of OOD samples. Compared to existing state-of-the-art methods, this approach achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) = 0.99. |
| title | Towards Robust Artificial Intelligence: Self-Supervised Learning Approach for Out-of-Distribution Detection |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.12713 |