Anomalous Example Detection in Deep Learning: A Survey

• URL: http://arxiv.org/abs/2003.06979v2
• Date: Fri, 19 Feb 2021 21:59:16 GMT
• Title: Anomalous Example Detection in Deep Learning: A Survey
• Authors: Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K. Varshney, Dawn Song
• Abstract summary: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for Deep Learning applications. We provide a taxonomy for existing techniques based on their underlying assumptions and adopted approaches. We highlight the unsolved research challenges while applying anomaly detection techniques in DL systems and present some high-impact future research directions.
• Score: 98.2295889723002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detection techniques to detect (and discard) these anomalous samples have been proposed in the recent past. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications. We provide a taxonomy for existing techniques based on their underlying assumptions and adopted approaches. We discuss various techniques in each of the categories and provide the relative strengths and weaknesses of the approaches. Our goal in this survey is to provide an easier yet better understanding of the techniques belonging to different categories in which research has been done on this topic. Finally, we highlight the unsolved research challenges while applying anomaly detection techniques in DL systems and present some high-impact future research directions.

Related papers

• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• Anomaly Detection in Graph Structured Data: A Survey [0.46040036610482665]
  We discuss a comprehensive overview of anomaly detection techniques on graph data. We present a new taxonomy that categorizes the different state-of-the-art anomaly detection methods.
  arXiv  Detail & Related papers  (2024-05-10T01:30:25Z)
• Weakly Supervised Anomaly Detection: A Survey [75.26180038443462]
  Anomaly detection (AD) is a crucial task in machine learning with various applications. We present the first comprehensive survey of weakly supervised anomaly detection (WSAD) methods. For each setting, we provide formal definitions, key algorithms, and potential future directions.
  arXiv  Detail & Related papers  (2023-02-09T10:27:21Z)
• Deep Learning for Time Series Anomaly Detection: A Survey [53.83593870825628]
  Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning.
  arXiv  Detail & Related papers  (2022-11-09T22:40:22Z)
• The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods [86.39044549664189]
  Anomaly detection algorithms for feature-vector data identify anomalies as outliers, but outlier detection has not worked well in deep learning. This paper proposes the Familiarity Hypothesis that these methods succeed because they are detecting the absence of familiar learned features rather than the presence of novelty. The paper concludes with a discussion of whether familiarity detection is an inevitable consequence of representation learning.
  arXiv  Detail & Related papers  (2022-03-04T18:32:58Z)
• A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods [3.198144010381572]
  This paper introduces the researchers of the field to a new perspective and reviews the recent deep-learning based semi-supervised video anomaly detection approaches. Our goal is to help researchers develop more effective video anomaly detection methods.
  arXiv  Detail & Related papers  (2021-11-02T14:00:33Z)
• Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review [28.752089275446462]
  generative adversarial networks (GANs) have attracted a great deal of attention in anomaly detection research. In this paper, we present a systematic literature review of the applications of GANs in anomaly detection.
  arXiv  Detail & Related papers  (2021-10-22T21:48:48Z)
• A Unifying Review of Deep and Shallow Anomaly Detection [38.202998314502786]
  We aim to identify the common underlying principles as well as the assumptions that are often made implicitly by various methods. We provide an empirical assessment of major existing methods that is enriched by the use of recent explainability techniques. We outline critical open challenges and identify specific paths for future research in anomaly detection.
  arXiv  Detail & Related papers  (2020-09-24T14:47:54Z)
• Deep Learning for Anomaly Detection: A Review [150.9270911031327]
  This paper surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in three high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages and disadvantages, and discuss how they address the aforementioned challenges.
  arXiv  Detail & Related papers  (2020-07-06T02:21:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.