yzhao062 / anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!
Why this job: topics: awesome-list, awesome
What it does
Outlier Detection _ (also known as *Anomaly Detection*) is an exciting yet challenging field, which aims to identify outlying objects that are deviant from the general data distribution. Outlier detection has been proven critical in many fields, such as credit card fraud analytics, network intrusion detection, and mechanical unit defect detection.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Inactive
- Last commit 8 months ago
- Stars
- 9,450
- +12 this week
- Licence
- AGPL-3.0
- Share-alike, even hosted
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- Python
- 100% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 52% closed
- 11 still open
- Pull requests
- 23 merged
- 3 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
Similar repos
| Repo | Stars | Last commit | Licence | Downloads for |
|---|---|---|---|---|
| lzz19980125 / awesome-time-series-segmentation-papers | 548 | 36 days ago | GPL-3.0 | No downloads |
| yueliu1999 / Awesome-Jailbreak-on-LLMs | 1,733 | 4 days ago | MIT | No downloads |
| steven2358 / awesome-generative-ai | 12,825 | 13 days ago | CC0-1.0 | No downloads |
More: languages, topics and project record
Languages
- Python 100%
Topics
- outlier-detection
- anomaly-detection
- outlier
- outlier-ensembles
- time-series-analysis
- data-mining
- awesome
- awesome-list
- unsupervised-learning
- fraud
- fraud-detection
- machine-learning
Project record
- JobLists and guides
- StatusListed
- Commits304
- Forks1,800
- Watching281
- Stars in 28 days+46
- CreatedMay 16, 2018
- First seen by usNov 7, 2026
- CheckedNov 8, 2026