Classification-Based Anomaly Detection for General Data

Liron Bergman; Yedid Hoshen

Classification-Based Anomaly Detection for General Data

Machine Learning 2020-05-06 v1 Computer Vision and Pattern Recognition Machine Learning

Authors: Liron Bergman , Yedid Hoshen

Abstract

Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The strong performance of our method is extensively validated on multiple datasets from different domains.

Keywords

anomaly detection generalized category discovery generalization in machine learning

Cite

@article{arxiv.2005.02359,
  title  = {Classification-Based Anomaly Detection for General Data},
  author = {Liron Bergman and Yedid Hoshen},
  journal= {arXiv preprint arXiv:2005.02359},
  year   = {2020}
}

Comments

ICLR'20

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