Out-of-Distribution Detection with Logical Reasoning,
04 Jan. 2024
(papers)
Our paper Out-of-Distribution Detction with Logical Reasoning has been accepted on the WACV 2024.
Abstract Machine Learning models often only generalize reliably to samples from the training distribution. Consequentially, detecting when input data is out-of-distribution (OOD) is crucial, especially in safety-critical applications. Current OOD detection methods, however, tend to be domain agnostic and often fail to incorporate valuable prior knowledge about the structure of the training distribution. To address this limitation, we introduce a novel, hybrid OOD detection algorithm that combines a deep learning-based perception system with a first-order logic-based knowledge representation.
224 Words, Tagged with: WACV · Anomaly Detection · Neuro-Symbolic