Mitigating Neural Network Overconfidence with Logit Normalization

Understand

Detecting out-of-distribution inputs is critical for safe deployment of machine learning models in the real world.

• However, neural networks are known to suffer from the overconfidence issue, where they produce abnormally high confidence for both in- and out-of-distribution inputs.
• In this work, we show that this issue can be mitigated through Logit Normalization (LogitNorm) -- a simple fix to the cross-entropy loss -- by enforcing a constant vector norm on the logits in training.
• Our method is motivated by the analysis that the norm of the logit keeps increasing during training, leading to overconfident output.