Classes Are Not Equal: An Empirical Study on Image Recognition Fairness

arXiv:2402.18133v1 Announce Type: new
Abstract: In this paper, we present an empirical study on image recognition fairness, i.e., extreme class accuracy disparity on balanced data like ImageNet. We experimentally demonstrate that classes are not equal and the fairness issue is prevalent for image classification models across various datasets, network architectures, and model capacities. Moreover, several intriguing properties of fairness are identified. First, the unfairness lies in problematic representation rather than classifier bias. Second, with the proposed concept of Model Prediction …

abstract accuracy arxiv class classification cs.cv cs.lg data datasets fairness image imagenet image recognition issue network paper recognition study type