Class Relevance Learning for Out-of-Distribution Detection

Image classification plays a pivotal role across diverse robotic applications, yet challenges persist when models are deployed in real-world scenarios. These models often fail to detect out-of-distribution (OOD) samples, classes not included in their training. This makes OOD detection a significant challenge for safe and effective real-world use. While existing techniques, like max logits, aim to leverage logits for OOD identification, they often disregard the intricate interclass relationships that underlie effective detection. This paper presents an innovative class relevance learning (CRL) method tailored for OOD detection. Our method establishes a comprehensive class relevance learning framework, strategically harnessing interclass relationships within the OOD pipeline. This framework significantly augments OOD detection capabilities. Extensive experimentation on diverse datasets, encompassing generic image classification datasets (Near OOD and Far OOD datasets), demonstrates the superiority of our method over state-of-the-art alternatives for OOD detection. The code is available on GitHub at: CRL.