International Business Machines Corporation
OUT-OF-DISTRIBUTION (OOD) DETECTION BY PERTURBATION

Abstract:

Improved quantification, detection, and characterizing of out-of-distribution (OOD) of a set of inputs that are generated by an alternative process such as anomalies, outliers, adversarial attacks, input errors can be provided with a combination of detection under perturbations and subset scanning algorithms. A first set of activations is extracted from nodes in a hidden layer of a neural network for an input. Noise is added to the input. A second set of activations is extracted from nodes in the hidden layer of a neural network for the noised input. A difference between the first set of activations and the second set of activations is determined. The difference is compared with a difference computed using in-distribution samples. Based on the comparison, an anomaly score for the input is determined. Multiple inputs can be processed. An iterative ascent algorithm finds out-of-distribution input and internal nodes with anomalous activations.