Out-of-Distribution Detection with Logical Reasoning (Extended Abstract)

Machine learning models often only generalize reliably to samples from their training distribution which motivates out-of-distribution (OOD) detection in safety-critical applications. Current OOD detection methods, however, tend to be domain agnostic and are incapable of incorporating prior knowledge about the structure of the training distribution. To address this limitation, we introduce a novel, neuro-symbolic OOD detection algorithm that combines a deep learning-based perception system with a first-order logic-based knowledge representation. A reasoning system uses this knowledge base at run-time to infer whether inputs are consistent with prior knowledge about the training distribution. This not only enhances performance but also fosters a level of explainability that is particularly beneficial in safety-critical contexts.