Anomaly detection resources: books, papers, videos, toolboxes
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This repository serves as a comprehensive, curated collection of resources for anomaly detection. It targets researchers, engineers, and practitioners in data science and machine learning, providing a centralized hub for learning materials, tools, and foundational papers in the field. The primary benefit is accelerated research and development by consolidating diverse, high-quality anomaly detection resources.
How It Works
The repository categorizes resources into books, tutorials, benchmarks, courses, papers (organized by sub-topic like time series, graph, deep learning), and toolboxes. It links to academic papers with abstracts and materials, popular libraries like PyOD and ADBench, and datasets. This structured approach allows users to quickly navigate and find relevant information for their specific anomaly detection needs.
Quick Start & Requirements
This is a curated list of resources, not a software package. No installation is required. Users can directly access linked papers, tutorials, and code repositories.
Highlighted Details
Maintenance & Community
The repository is maintained by yzhao062, with contributions encouraged via issues and pull requests. The author also highlights their related projects like PyOD, ADBench, and AD-LLM.
Licensing & Compatibility
The repository itself is licensed under the MIT License, permitting broad use and distribution. Individual linked resources may have their own licenses.
Limitations & Caveats
As a curated list, the repository's content is dependent on the availability and maintenance of the linked external resources. The breadth of coverage is extensive, but specific niche areas might be less represented.
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