A curated collection of anomaly detection resources (books, papers, courses, datasets, tools) with links and benchmarks focused on outlier detection, LLM/LLM-related work, and related toolkits. The repo aggregates resources across multiple subtopics and stays updated through 2025.
Collecting history — the radar snapshots this repo daily. The trend line appears after 3 days of data (1 so far).
What it is
An open repository that collects anomaly detection resources, including books, academic papers, online courses and videos, datasets, and open-source libraries/toolkits. It emphasizes topics such as outlier detection, time-series analysis, graph neural networks, and large-language-models related to anomaly detection.
How it works
Contents are organized into sections like Books & Tutorials & Benchmarks, Courses/Seminars/Videos, and Toolbox & Datasets. It provides curated lists with brief descriptions and external links to materials and repositories. The README includes references to notable resources (e.g., PyOD, PyTOD, PySAD, SUOD) and mentions related benchmarks such as NLP-ADBench and ADBench.
Getting started
The repository appears to be a reference list; no installation or run instructions are provided in the README excerpt. Commands or setup steps are not shown here beyond linking to individual projects.
Recent releases
The latest releases section states: "latest 0: - none" indicating no formal releases.
Traction
The repository has 9362 stars and 1804 forks, with 14 open issues. These numbers are present in FACTS as:
- stars: 9362
- forks: 1804
- open_issues: 14
Behind the repo
Not applicable here; no startup/company link is provided in the excerpt.
Caveats
- License: AGPL-3.0
- Created: 2018-05-16
- Last push: 2026-03-02
- Language: Python
- Topics include anomaly-detection, awesome-list, data-mining, fraud, LLM/VLM related terms