Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations
This addresses a reliability issue for deploying machine learning models in real-world scenarios, though it is incremental as it builds on existing OOD detection baselines.
The paper tackles the problem of overconfidence in out-of-distribution (OOD) detection, where neural networks produce highly confident predictions for OOD inputs, by measuring extreme activations in the penultimate layer to improve detection, resulting in double-digit increases in OOD detection AUC without harming performance.
Detecting out-of-distribution (OOD) instances is crucial for the reliable deployment of machine learning models in real-world scenarios. OOD inputs are commonly expected to cause a more uncertain prediction in the primary task; however, there are OOD cases for which the model returns a highly confident prediction. This phenomenon, denoted as "overconfidence", presents a challenge to OOD detection. Specifically, theoretical evidence indicates that overconfidence is an intrinsic property of certain neural network architectures, leading to poor OOD detection. In this work, we address this issue by measuring extreme activation values in the penultimate layer of neural networks and then leverage this proxy of overconfidence to improve on several OOD detection baselines. We test our method on a wide array of experiments spanning synthetic data and real-world data, tabular and image datasets, multiple architectures such as ResNet and Transformer, different training loss functions, and include the scenarios examined in previous theoretical work. Compared to the baselines, our method often grants substantial improvements, with double-digit increases in OOD detection AUC, and it does not damage performance in any scenario.