LogitAC: Logit Amplitude Constraints for Confidence Calibration and Out-of-Distribution Detection

Discriminating out-of-distribution (OOD) input is critical for safely deploying deep learning models in reality. A primary challenge in OOD detection is that the model produces abnormally high prediction-confidence scores for both in-distribution (ID) and OOD samples. The loss function forces the logits to increase abnormally during training, leading to overconfident output in the model. Our work shows that a logit amplitude constraint and an advanced training strategy can mitigate this issue. By constraining the amplitude of the logits during training, the distribution of confidence scores between ID and OOD samples can be separated, thus improving detection efficiency. Experiments demonstrate that our method greatly improves the performance of existing OOD detection methods based on maximum softmax probability scores. FPR95 was reduced by an average of 4.86% in general benchmarks. The proposed approach does not require any model modifications or access to OOD instances and can easily be applied to existing OOD detection methods.