SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance
This provides a strong, easy-to-use baseline for OoD detection, which is important for improving model reliability in applications like safety-critical systems, though it appears incremental as it simplifies existing methods.
The paper tackles the problem of Out-of-Distribution (OoD) detection by showing that a simple method using ResNet18 trained with Supervised Contrastive Learning and Euclidean distance achieves state-of-the-art results on benchmarks, potentially outperforming more complex approaches.
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.