Improving Out-of-Distribution Detection with Markov Logic Networks

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.

@article{kirchheim2025_2506.04241,
  title={ Improving Out-of-Distribution Detection with Markov Logic Networks },
  author={ Konstantin Kirchheim and Frank Ortmeier },
  journal={arXiv preprint arXiv:2506.04241},
  year={ 2025 }
}