Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output

TL;DR

This paper introduces a simple yet effective method for detecting out-of-distribution inputs in deep neural networks by training a one-class classifier on early-layer outputs, improving detection performance without requiring OOD samples.

Contribution

The paper presents a novel OOD detection technique that leverages early-layer outputs and can be integrated into existing classifiers without access to OOD data.

Findings

Outperforms state-of-the-art OOD detection methods across multiple datasets.

Does not require OOD samples for training or detection.

Applicable to both low- and high-dimensional data.

Abstract

Deep neural networks achieve superior performance in challenging tasks such as image classification. However, deep classifiers tend to incorrectly classify out-of-distribution (OOD) inputs, which are inputs that do not belong to the classifier training distribution. Several approaches have been proposed to detect OOD inputs, but the detection task is still an ongoing challenge. In this paper, we propose a new OOD detection approach that can be easily applied to an existing classifier and does not need to have access to OOD samples. The detector is a one-class classifier trained on the output of an early layer of the original classifier fed with its original training set. We apply our approach to several low- and high-dimensional datasets and compare it to the state-of-the-art detection approaches. Our approach achieves substantially better results over multiple metrics.