Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features

TL;DR

This paper introduces a subspace-based probabilistic modeling approach for out-of-distribution detection in deep neural networks, utilizing dimensionality reduction to improve detection accuracy and computational efficiency.

Contribution

It applies linear and nonlinear subspace techniques to deep features, enhancing OOD detection by capturing true feature subspaces and using feature reconstruction error.

Findings

Lower-dimensional embeddings improve OOD detection.

Feature reconstruction error can outperform likelihood scores.

Effective on CIFAR10, CIFAR100, and SVHN datasets.

Abstract

This paper presents a principled approach for detecting out-of-distribution (OOD) samples in deep neural networks (DNN). Modeling probability distributions on deep features has recently emerged as an effective, yet computationally cheap method to detect OOD samples in DNN. However, the features produced by a DNN at any given layer do not fully occupy the corresponding high-dimensional feature space. We apply linear statistical dimensionality reduction techniques and nonlinear manifold-learning techniques on the high-dimensional features in order to capture the true subspace spanned by the features. We hypothesize that such lower-dimensional feature embeddings can mitigate the curse of dimensionality, and enhance any feature-based method for more efficient and effective performance. In the context of uncertainty estimation and OOD, we show that the log-likelihood score obtained from the distributions learnt on this lower-dimensional subspace is more discriminative for OOD detection. We also show that the feature reconstruction error, which is the $L_2$-norm of the difference between the original feature and the pre-image of its embedding, is highly effective for OOD detection and in some cases superior to the log-likelihood scores. The benefits of our approach are demonstrated on image features by detecting OOD images, using popular DNN architectures on commonly used image datasets such as CIFAR10, CIFAR100, and SVHN.