COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

arXiv:2403.06874v1 Announce Type: cross
Abstract: High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper, we focus on the species recognition task in images concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing …

abstract anomaly arxiv class classification cs.cv cs.lg databases detection distribution focus hierarchical images multiple novel paper practical recognition scale species type