Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
This addresses the challenge of OOD detection for deep learning systems, which is crucial for safety and reliability, though it appears incremental as it builds on existing detection methods.
The paper tackles the problem of deep neural networks incorrectly classifying out-of-distribution (OOD) inputs by proposing a new detection approach using an early-layer output as a one-class classifier, achieving substantially better results over multiple metrics compared to state-of-the-art methods.
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.