Support Vector Data Description

TL;DR: This article proposes a method called Isolation Forest (iForest), which detects anomalies purely based on the concept of isolation without employing any distance or density measure---fundamentally different from all existing methods.

TL;DR: This paper introduces a new anomaly detection method—Deep Support Vector Data Description—, which is trained on an anomaly detection based objective and shows the effectiveness of the method on MNIST and CIFAR-10 image benchmark datasets as well as on the detection of adversarial examples of GTSRB stop signs.

TL;DR: This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, representations of models, datasets, evaluation criteria, and algorithm comparisons to highlight the limitations of existing approaches and point out some promising subsequent research directions.

TL;DR: In this article, a two-stage framework is proposed for defect detection using normal training data only, where self-supervised deep representations are first learned and then a generative one-class classifier is built on learned representations.