A Generative Approach to Open Set Recognition Using Distance-Based Probabilistic Anomaly Augmentation

Abstract

Machine learning (ML) algorithms that are used in decision support (DS) and autonomous systems commonly train on labeled categorical samples from a closed set. This, however, poses a problem for deployed DS and autonomous systems when they encounter an anomalous pattern that did not originate from the closed set distribution used for training. In this case, the ML algorithm that was trained only on closed set samples may erroneously identify an anomalous pattern as having originated from one of the categories in the closed set, sometimes with very high confidence. In this paper, we consider the problem of unknown pattern recognition from a generative perspective in which additional synthetic training samples that represent anomalies are added to the training data. These synthetic samples are generated to optimally balance the desire to place anomalies all along the boundary of the training set in feature space, while not adversely effecting core classification performance on the test set. We demonstrate the efficacy of distance-based probabilistic anomaly augmentation (DPAA) that is proposed in this paper for a diverse set of applications such as character recognition and intrusion detection, and compare its combined classification and identification performance to both recent open set and more traditional novelty detection approaches.