Improving Out-of-Distribution Detection with Markov Logic Networks
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912414288576512 |
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| author | Kirchheim, Konstantin Ortmeier, Frank |
| author_facet | Kirchheim, Konstantin Ortmeier, Frank |
| contents | Out-of-distribution (OOD) detection is essential for ensuring the reliability of deep learning models operating in open-world scenarios. Current OOD detectors mainly rely on statistical models to identify unusual patterns in the latent representations of a deep neural network. This work proposes to augment existing OOD detectors with probabilistic reasoning, utilizing Markov logic networks (MLNs). MLNs connect first-order logic with probabilistic reasoning to assign probabilities to inputs based on weighted logical constraints defined over human-understandable concepts, which offers improved explainability. Through extensive experiments on multiple datasets, we demonstrate that MLNs can significantly enhance the performance of a wide range of existing OOD detectors while maintaining computational efficiency. Furthermore, we introduce a simple algorithm for learning logical constraints for OOD detection from a dataset and showcase its effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04241 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Out-of-Distribution Detection with Markov Logic Networks Kirchheim, Konstantin Ortmeier, Frank Machine Learning Out-of-distribution (OOD) detection is essential for ensuring the reliability of deep learning models operating in open-world scenarios. Current OOD detectors mainly rely on statistical models to identify unusual patterns in the latent representations of a deep neural network. This work proposes to augment existing OOD detectors with probabilistic reasoning, utilizing Markov logic networks (MLNs). MLNs connect first-order logic with probabilistic reasoning to assign probabilities to inputs based on weighted logical constraints defined over human-understandable concepts, which offers improved explainability. Through extensive experiments on multiple datasets, we demonstrate that MLNs can significantly enhance the performance of a wide range of existing OOD detectors while maintaining computational efficiency. Furthermore, we introduce a simple algorithm for learning logical constraints for OOD detection from a dataset and showcase its effectiveness. |
| title | Improving Out-of-Distribution Detection with Markov Logic Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.04241 |