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

• URL: http://arxiv.org/abs/2109.05493v1
• Date: Sun, 12 Sep 2021 11:38:04 GMT
• Title: LEA-Net: Layer-wise External Attention Network for Efficient Color Anomaly Detection
• Authors: Ryoya Katafuchi and Terumasa Tokunaga
• Abstract summary: We propose a novel model called Layer-wise External Attention Network (LEA-Net) for efficient image anomaly detection. Our strategy is as follows: (i) Prior knowledge about anomalies is represented as the anomaly map generated by unsupervised learning of normal instances, (ii) The anomaly map is translated to an attention map by the external network, (iii) The attention map is then incorporated into intermediate layers of the anomaly detection network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The utilization of prior knowledge about anomalies is an essential issue for anomaly detections. Recently, the visual attention mechanism has become a promising way to improve the performance of CNNs for some computer vision tasks. In this paper, we propose a novel model called Layer-wise External Attention Network (LEA-Net) for efficient image anomaly detection. The core idea relies on the integration of unsupervised and supervised anomaly detectors via the visual attention mechanism. Our strategy is as follows: (i) Prior knowledge about anomalies is represented as the anomaly map generated by unsupervised learning of normal instances, (ii) The anomaly map is translated to an attention map by the external network, (iii) The attention map is then incorporated into intermediate layers of the anomaly detection network. Notably, this layer-wise external attention can be applied to any CNN model in an end-to-end training manner. For a pilot study, we validate LEA-Net on color anomaly detection tasks. Through extensive experiments on PlantVillage, MVTec AD, and Cloud datasets, we demonstrate that the proposed layer-wise visual attention mechanism consistently boosts anomaly detection performances of an existing CNN model, even on imbalanced datasets. Moreover, we show that our attention mechanism successfully boosts the performance of several CNN models.

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