Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/131343
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Type: | Conference paper |
Title: | Deep learning for anomaly detection: challenges, methods, and opportunities |
Author: | Pang, G. Cao, L. Aggarwal, C. |
Citation: | Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WDSM'21), 2021, pp.1127-1130 |
Publisher: | Association for Computing Machinery |
Publisher Place: | New York, NY |
Issue Date: | 2021 |
ISBN: | 9781450382977 |
Conference Name: | ACM International Conference on Web Search and Data Mining (WSDM) (8 Mar 2021 - 12 Mar 2021 : virtual online) |
Statement of Responsibility: | Guansong Pang, Longbing Cao, Charu Aggarwal |
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 |
Rights: | © 2021 Association for Computing Machinery. |
DOI: | 10.1145/3437963.3441659 |
Published version: | https://dl.acm.org/doi/proceedings/10.1145/3437963 |
Appears in Collections: | Aurora harvest 4 Australian Institute for Machine Learning publications |
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