Awesome Anomaly Detection
A curated list of research papers, datasets, and resources for anomaly detection in time-series, video, and image data.
What is Awesome Anomaly Detection?
Awesome Anomaly Detection is a curated GitHub repository listing academic papers, datasets, and resources for detecting anomalies—unusual patterns that deviate from expected behavior—in data. It addresses the need for a centralized, organized reference in a rapidly evolving research field, covering time-series, video, and image data across tasks like novelty detection, outlier detection, and one-class classification.
Machine learning researchers, data scientists, and engineers working on anomaly detection projects, particularly those exploring unsupervised or semi-supervised methods for industrial inspection, surveillance, fraud detection, or system monitoring.
It saves significant literature review time by aggregating and categorizing hundreds of papers from top venues, provides a clear taxonomy of methods, and includes links to code and datasets for practical implementation, making it a go-to starting point for both newcomers and experts.
Overview
A curated list of awesome anomaly detection resources
Use Cases
Best For
- Researchers conducting literature reviews on state-of-the-art anomaly detection methods
Related Projects
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