# Deep Learning for Anomaly Detection: A Review

**Authors:** Guansong Pang, Chunhua Shen, Longbing Cao, Anton van den Hengel  
**arXiv:** [2007.02500](https://arxiv.org/abs/2007.02500)  
**Date:** 2020-07-06  
**Summary page:** https://www.summarizepaper.com/en/paper/2007.02500v3/

## Key points

- Anomaly detection, also known as outlier detection or novelty detection, is a longstanding and active research area
- Deep learning has emerged as a critical direction in anomaly detection
- The paper categorizes advancements in deep anomaly detection into three high-level categories and 11 fine-grained categories of methods
- The authors review key intuitions, objective functions, underlying assumptions, advantages, and disadvantages of these methods
- Deep anomaly detection addresses challenges posed by anomaly detection problems
- Strengths and weaknesses of different approaches are highlighted to provide insights into their effectiveness in handling various complexities
- Possible future opportunities and new perspectives for addressing challenges in deep anomaly detection are explored
- Potential areas for improvement and directions for further research are discussed to guide researchers with a roadmap for future exploration
- The paper serves as a comprehensive guide to understanding state-of-the-art techniques in deep anomaly detection
- It offers valuable insights into advancements made in this field
- Provides researchers with an invaluable resource to make informed decisions about their work going forward

## Summary

The paper titled "Deep Learning for Anomaly Detection: A Review" provides a comprehensive survey of the research on deep anomaly detection. Anomaly detection, also known as outlier detection or novelty detection, has been a longstanding and active research area in various communities for several decades. However, there are still unique complexities and challenges that require advanced approaches. In recent years, deep learning has emerged as a critical direction in anomaly detection. The paper categorizes the advancements in deep anomaly detection into three high-level categories and 11 fine-grained categories of methods. The authors review the key intuitions, objective functions, underlying assumptions, advantages, and disadvantages of these methods. The paper discusses how deep anomaly detection addresses the challenges posed by anomaly detection problems and highlights the strengths and weaknesses of different approaches to provide insights into their effectiveness in handling various complexities. Furthermore, the authors explore possible future opportunities and new perspectives for addressing the challenges in deep anomaly detection. They discuss potential areas for improvement and suggest directions for further research to guide researchers with a roadmap for future exploration. Overall, this paper serves as a comprehensive guide to understanding the state-of-the-art techniques in deep anomaly detection. It offers valuable insights into the advancements made in this field and provides researchers with an invaluable resource to help them make informed decisions about their work going forward.