Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2021

Abstract

In this tutorial we aim to present a comprehensive survey of the advances in deep learning techniques specifically designed for anomaly detection (deep anomaly detection for short). Deep learning has gained tremendous success in transforming many data mining and machine learning tasks, but popular deep learning techniques are inapplicable to anomaly detection due to some unique characteristics of anomalies, e.g., rarity, heterogeneity, boundless nature, and prohibitively high cost of collecting large-scale anomaly data. Through this tutorial, audiences would gain a systematic overview of this area, learn the key intuitions, objective functions, underlying assumptions, advantages and disadvantages of different categories of state-of-the-art deep anomaly detection methods, and recognize its broad real-world applicability in diverse domains. We also discuss what challenges the current deep anomaly detection methods can address and envision this area from multiple different perspectives. Any audience who may be interested in deep learning, anomaly/outlier/novelty detection, out-of-distribution detection, representation learning with limited labeled data, and self-supervised representation learning would find it very helpful in attending this tutorial. Researchers and practitioners in finance, cybersecurity, healthcare would also find the tutorial helpful in practice.

Keywords

anomaly detection; deep learning; neural networks; outlier detection; representation learning; novelty detection

Discipline

Artificial Intelligence and Robotics | OS and Networks

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 14th ACM International Conference on Web Search Data Mining, Virtual Conference, 2021 March 8-12

First Page

1127

Last Page

1130

ISBN

9781450382977

Identifier

10.1145/3437963.3441659

Publisher

ACM

City or Country

Virtual Conference

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