# Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/divide-and-assemble-learning-block-wise-memory-for-unsupervised-anomaly/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Jinlei Hou,Yingying Zhang,Qiaoyong Zhong,Di Xie,Shiliang Pu,Hong Zhou |
| Citations | 183 |
| DOI | 10.1109/iccv48922.2021.00867 |
| Fields | Computer Science,Medicine |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3186300274 |
| Type | conference-paper |
| Year | 2021 |

## Paper authors

- [Hong Zhou](https://scholariq.org/researchers/hong-zhou/)

## Paper primary topic

- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)

## Paper topics

- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)
- [Data-Driven Disease Surveillance](https://scholariq.org/topics/data-driven-disease-surveillance/)
- [Network Security and Intrusion Detection](https://scholariq.org/topics/network-security-and-intrusion-detection/)

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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.