Out-of-Distribution Detection

Detect Out-of-Distribution Inputs Before Trusting a Model

A model can produce a confident-looking prediction for an input that is unlike anything it was designed to handle. A product classifier trained on shoes, shirts, and bags still has to return some class when given a photo of a bicycle. The classifier’s output layer does not automatically gain an unknown class just because the input is unfamiliar. This is the problem addressed by out-of-distribution detection, usually shortened to OOD detection. The goal is to recognize inputs that differ meaningfully from the data the model is expected to handle, before the application treats an ordinary model prediction as trustworthy.