Pseudo-label Induced Subspace Representation Learning for Robust Out-of-Distribution Detection
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912520925609984 |
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| author | Azad, Tarhib Al Sayem, Faizul Rakib Ibrahim, Shahana |
| author_facet | Azad, Tarhib Al Sayem, Faizul Rakib Ibrahim, Shahana |
| contents | Out-of-distribution (OOD) detection lies at the heart of robust artificial intelligence (AI), aiming to identify samples from novel distributions beyond the training set. Recent approaches have exploited feature representations as distinguishing signatures for OOD detection. However, most existing methods rely on restrictive assumptions on the feature space that limit the separability between in-distribution (ID) and OOD samples. In this work, we propose a novel OOD detection framework based on a pseudo-label-induced subspace representation, that works under more relaxed and natural assumptions compared to existing feature-based techniques. In addition, we introduce a simple yet effective learning criterion that integrates a cross-entropy-based ID classification loss with a subspace distance-based regularization loss to enhance ID-OOD separability. Extensive experiments validate the effectiveness of our framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03108 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pseudo-label Induced Subspace Representation Learning for Robust Out-of-Distribution Detection Azad, Tarhib Al Sayem, Faizul Rakib Ibrahim, Shahana Machine Learning Artificial Intelligence Out-of-distribution (OOD) detection lies at the heart of robust artificial intelligence (AI), aiming to identify samples from novel distributions beyond the training set. Recent approaches have exploited feature representations as distinguishing signatures for OOD detection. However, most existing methods rely on restrictive assumptions on the feature space that limit the separability between in-distribution (ID) and OOD samples. In this work, we propose a novel OOD detection framework based on a pseudo-label-induced subspace representation, that works under more relaxed and natural assumptions compared to existing feature-based techniques. In addition, we introduce a simple yet effective learning criterion that integrates a cross-entropy-based ID classification loss with a subspace distance-based regularization loss to enhance ID-OOD separability. Extensive experiments validate the effectiveness of our framework. |
| title | Pseudo-label Induced Subspace Representation Learning for Robust Out-of-Distribution Detection |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.03108 |