Gradient-Regularized Out-of-Distribution Detection

TL;DR

This paper introduces a gradient-based approach for out-of-distribution detection in neural networks, utilizing loss gradients and an energy-based sampling method to improve detection accuracy and robustness.

Contribution

It proposes leveraging loss gradients and a novel energy-based sampling technique to enhance OOD detection, with theoretical analysis supporting the method's robustness.

Findings

Improved state-of-the-art FPR95 by 4% on ImageNet.

Effective use of gradient information for local OOD detection.

Theoretical foundation via Lipschitz and robustness analysis.

Abstract

One of the challenges for neural networks in real-life applications is the overconfident errors these models make when the data is not from the original training distribution. Addressing this issue is known as Out-of-Distribution (OOD) detection. Many state-of-the-art OOD methods employ an auxiliary dataset as a surrogate for OOD data during training to achieve improved performance. However, these methods fail to fully exploit the local information embedded in the auxiliary dataset. In this work, we propose the idea of leveraging the information embedded in the gradient of the loss function during training to enable the network to not only learn a desired OOD score for each sample but also to exhibit similar behavior in a local neighborhood around each sample. We also develop a novel energy-based sampling method to allow the network to be exposed to more informative OOD samples during the training phase. This is especially important when the auxiliary dataset is large. We demonstrate the effectiveness of our method through extensive experiments on several OOD benchmarks, improving the existing state-of-the-art FPR95 by 4% on our ImageNet experiment. We further provide a theoretical analysis through the lens of certified robustness and Lipschitz analysis to showcase the theoretical foundation of our work. Our code is available at https://github.com/o4lc/Greg-OOD.