Class Relevance Learning For Out-of-distribution Detection
Butian Xiong Liguang Zhou Tin Lun Lam Yangsheng Xu
Image classification plays a pivotal role across diverse applications, yet
challenges persist when models are deployed in real-world scenarios. Notably,
these models falter in detecting unfamiliar classes that were not incorporated
during classifier training, a formidable hurdle for safe and effective
real-world model deployment, commonly known as out-of-distribution (OOD)
detection. 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 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.