Multiscale Score Matching for Out-of-Distribution Detection

TL;DR

This paper introduces a novel unsupervised multiscale score matching method that detects out-of-distribution images by analyzing score norms at multiple noise levels, outperforming existing techniques.

Contribution

It proposes a simple, effective approach combining score estimation at multiple noise scales with an auxiliary model for OOD detection, advancing the state-of-the-art.

Findings

Significantly outperforms previous OOD detection methods.

Effectively separates CIFAR-10 and SVHN images.

Uses a straightforward training scheme with deep score networks.

Abstract

We present a new methodology for detecting out-of-distribution (OOD) images by utilizing norms of the score estimates at multiple noise scales. A score is defined to be the gradient of the log density with respect to the input data. Our methodology is completely unsupervised and follows a straight forward training scheme. First, we train a deep network to estimate scores for levels of noise. Once trained, we calculate the noisy score estimates for N in-distribution samples and take the L2-norms across the input dimensions (resulting in an NxL matrix). Then we train an auxiliary model (such as a Gaussian Mixture Model) to learn the in-distribution spatial regions in this L-dimensional space. This auxiliary model can now be used to identify points that reside outside the learned space. Despite its simplicity, our experiments show that this methodology significantly outperforms the state-of-the-art in detecting out-of-distribution images. For example, our method can effectively separate CIFAR-10 (inlier) and SVHN (OOD) images, a setting which has been previously shown to be difficult for deep likelihood models.