Out-of-Distribution Detection for Contrastive Models Using Angular Distance Measures

M. Shifat Hossain, Sumit Kumar Jha, Chase Walker, Rickard Ewetz

Abstract: Vision-language models have demonstrated extraordinary zero-shot image classification capabilities. Out-of-distribution (OOD) detection is the problem of determining if a model is operating within its knowledge limits. While distance-based detection algorithms have emerged as a promising approach to OOD detection, we observe that there is a disparity between the distance measures used for OOD detection and model training. Recent studies have attempted to mitigate this shortcoming by modifying the contrastive learning process, which is highly undesirable for foundation models. In this paper, we propose an Angular distance-based out-of-distribution detection method for Contrastive models (AEC), an OOD detection framework for foundational contrastive models based on an angular distance measure. The angular distance-based score is compliant with the standard training process and circumvents the need to modify the training process of the model. We also formulate a distance transformation and solve an optimization problem to determine an OOD score threshold value for in-distribution and out-of-distribution data. The experimental evaluation demonstrates that AEC outperforms state-of-the-art OOD detection models in terms of AUROC, FPR@95TPR, accuracy, and correct ID metrics. We obtained an overall AUROC and FPR@95TPR of 70.42 and 83.98 from the proposed algorithm, which is significantly better compared to the SOTA OOD detection algorithms.