SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance

Abstract

Out-of-Distribution (OoD) detection has developed substantially in the past few years, with available methods approaching, and in a few cases achieving, perfect data separation on standard benchmarks. These results generally involve large or complex models, pretraining, exposure to OoD examples or extra hyperparameter tuning. Remarkably, it is possible to achieve results that can exceed many of these state-of-the-art methods with a very simple method. We demonstrate that ResNet18 trained with Supervised Contrastive Learning (SCL) produces state-of-the-art results out-of-the-box on near and far OoD detection benchmarks using only Euclidean distance as a scoring rule. This may obviate the need in some cases for more sophisticated methods or larger models, and at the very least provides a very strong, easy to use baseline for further experimentation and analysis.