Title: DAVIS: Out-of-Distribution Detection via Dominant Activations and Variance for Increased Separation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: AI Safety, Out-of-distribution Detection, Anomaly detection

Abstract: Detecting out-of-distribution (OOD) inputs is a critical safeguard for deploying machine learning models in the real world. However, most post-hoc detection methods operate on penultimate feature representations derived from global average pooling (GAP) – a lossy operation that discards valuable distributional statistics from activation maps prior to global average pooling. We contend that these overlooked statistics, particularly channel-wise variance and dominant (maximum) activations, are highly discriminative for OOD detection. We introduce DAVIS, a simple and broadly applicable post-hoc technique that enriches feature vectors by incorporating these crucial statistics, directly addressing the information loss from GAP. Extensive evaluations show DAVIS sets a new benchmark across diverse architectures, including ResNet, DenseNet, and EfficientNet. It achieves significant reductions in the false positive rate (FPR95), with improvements of 48.26% on CIFAR-10 using ResNet-18, 38.13% on CIFAR-100 using ResNet-34, and 26.83% on ImageNet-1k benchmarks using MobileNet-v2. Our analysis reveals the underlying mechanism for this improvement, providing a principled basis for moving beyond the mean in OOD detection. Our code is available here: https://github.com/epsilon-2007/DAVIS

TL;DR: We propose DAVIS, a post hoc OOD detection method that leverages variance and dominant values within activation channels to enhance ID–OOD separability.