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

Understand

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.