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This review aims to explore the advancements in deep anomaly detection and categorize existing methods. It highlights the unique challenges in the field and potential future directions for research.

July 6, 2020Open Access

Deep Learning for Anomaly Detection: A Review

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This review aims to explore the advancements in deep anomaly detection and categorize existing methods. It highlights the unique challenges in the field and potential future directions for research.
Comprehensive survey of deep anomaly detection methods categorized into three high-level and 11 fine-grained groups.
Analysis of key intuitions, objective functions, and underlying assumptions for various techniques.
Discussion of advantages, disadvantages, and future research opportunities in deep anomaly detection.
Identified significant advancements in deep anomaly detection across various categories.
Outlined key advantages and limitations of existing methodologies in addressing complex challenges.
Proposed future research opportunities to enhance the effectiveness of deep anomaly detection techniques.

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Review surveys advancements in deep anomaly detection techniques, exploring challenges and future directions.

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Pang et al. (2020) studied this question.

synapsesocial.com/papers/6a554c49797fa3705b36876b https://doi.org/10.48550/arxiv.2007.02500

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Deep Learning for Anomaly Detection: A Review

Key Points

Discussion

Implication

Cite This Study

Deep Learning for Anomaly Detection: A Review

Key Points

Discussion

Implication

Cite This Study

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