# Anomaly Detection Techniques and Applications

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/anomaly-detection-techniques-and-applications-2/

## Facts

| Field | Value |
| --- | --- |
| Citations | 879,178 |
| Description | This cluster of papers focuses on the detection of anomalies in high-dimensional data, particularly in the context of video analysis, surveillance, and time series data. It covers a wide range of techniques including unsupervised learning, outlier detection, deep learning, and novelty detection for identifying abnormal patterns and events. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | https://openalex.org/T11512 |
| Works | 78,137 |

## Topic researchers

Showing 12 of 20.

- [Kaiming He](https://scholariq.org/researchers/kaiming-he/)
- [Yoshua Bengio](https://scholariq.org/researchers/yoshua-bengio/)
- [Geoffrey E. Hinton](https://scholariq.org/researchers/geoffrey-e-hinton/)
- [Ross Girshick](https://scholariq.org/researchers/ross-girshick/)
- [Xiangyu Zhang](https://scholariq.org/researchers/xiangyu-zhang/)
- [Jian Sun](https://scholariq.org/researchers/jian-sun-2/)
- [Andrew Zisserman](https://scholariq.org/researchers/andrew-zisserman/)
- [Xiaogang Wang](https://scholariq.org/researchers/xiaogang-wang/)
- [Yann LeCun](https://scholariq.org/researchers/yann-lecun/)
- [Tien Yin Wong](https://scholariq.org/researchers/tien-yin-wong/)
- [Vladimir Vapnik](https://scholariq.org/researchers/vladimir-vapnik/)
- [Li Fei-Fei](https://scholariq.org/researchers/li-fei-fei/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.