On the Importance of Regularisation and Auxiliary Information in OOD Detection

Abstract

Neural networks are often utilised in critical domain applications, even though they exhibit overconfident predictions for ambiguous inputs. This deficiency demonstrates a fundamental flaw: that neural networks often overfit on spurious correlations. We address this limitation by presenting two novel objectives that improve out-of-distribution (OOD) detection. We empirically demonstrate that our methods outperform the baseline while still maintaining a competitive performance against the rest. Additionally, we empirically demonstrate the robustness of our approach against common corruptions and the importance of regularisation and auxiliary information in OOD detection.