Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investigates the use of contrastive training to boost OOD detection performance. Unlike leading methods for OOD detection, our approach does not require access to examples labeled explicitly as OOD, which can be difficult to collect in practice. We show in extensive experiments that contrastive training significantly helps OOD detection performance on a number of common benchmarks. By introducing and employing the Confusion Log Probability (CLP) score, which quantifies the difficulty of the OOD detection task by capturing the similarity of inlier and outlier datasets, we show that our method especially improves performance in the `near OOD' classes -- a particularly challenging setting for previous methods.
@article{arxiv.2007.05566,
title = {Contrastive Training for Improved Out-of-Distribution Detection},
author = {Jim Winkens and Rudy Bunel and Abhijit Guha Roy and Robert Stanforth and Vivek Natarajan and Joseph R. Ledsam and Patricia MacWilliams and Pushmeet Kohli and Alan Karthikesalingam and Simon Kohl and Taylan Cemgil and S. M. Ali Eslami and Olaf Ronneberger},
journal= {arXiv preprint arXiv:2007.05566},
year = {2020}
}