Gradient Short-Circuit: Efficient Out-of-Distribution Detection via Feature Intervention

TL;DR

This paper introduces a novel, efficient method for out-of-distribution detection that leverages gradient behavior during inference to distinguish between ID and OOD samples, improving robustness with minimal computational overhead.

Contribution

The paper proposes a gradient short-circuit technique that intervenes at inference time to improve OOD detection by exploiting local gradient patterns, with a local approximation to avoid extra forward passes.

Findings

Significant improvement on standard OOD benchmarks

Method is lightweight and compatible with existing inference pipelines

Achieves robust OOD detection with minimal computational cost

Abstract

Out-of-Distribution (OOD) detection is critical for safely deploying deep models in open-world environments, where inputs may lie outside the training distribution. During inference on a model trained exclusively with In-Distribution (ID) data, we observe a salient gradient phenomenon: around an ID sample, the local gradient directions for "enhancing" that sample's predicted class remain relatively consistent, whereas OOD samples--unseen in training--exhibit disorganized or conflicting gradient directions in the same neighborhood. Motivated by this observation, we propose an inference-stage technique to short-circuit those feature coordinates that spurious gradients exploit to inflate OOD confidence, while leaving ID classification largely intact. To circumvent the expense of recomputing the logits after this gradient short-circuit, we further introduce a local first-order approximation that accurately captures the post-modification outputs without a second forward pass. Experiments on standard OOD benchmarks show our approach yields substantial improvements. Moreover, the method is lightweight and requires minimal changes to the standard inference pipeline, offering a practical path toward robust OOD detection in real-world applications.