# LEA-Net: Layer-wise External Attention Network for Efficient Color Anomaly Detection

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/lea-net-layer-wise-external-attention-network-for-efficient-color-anomaly/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Ryoya Katafuchi,T Tokunaga |
| Citations | 2 |
| DOI | 10.48550/arxiv.2109.05493 |
| Fields | Computer Science,Engineering,Medicine |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2109.05493 |
| OpenAlex ID | https://openalex.org/W3200560057 |
| Type | preprint |
| Year | 2021 |

## Paper authors

- [Ryoya Katafuchi](https://scholariq.org/researchers/ryoya-katafuchi/)

## Paper journal

- [arXiv (Cornell University)](https://scholariq.org/journals/arxiv-cornell-university/)

## 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/)
- [Mosquito-borne diseases and control](https://scholariq.org/topics/mosquito-borne-diseases-and-control/)
- [Remote-Sensing Image Classification](https://scholariq.org/topics/remote-sensing-image-classification/)

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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.