ATAC-Net: Zoomed view works better for Anomaly Detection

• URL: http://arxiv.org/abs/2406.14398v1
• Date: Thu, 20 Jun 2024 15:18:32 GMT
• Title: ATAC-Net: Zoomed view works better for Anomaly Detection
• Authors: Shaurya Gupta, Neil Gautam, Anurag Malyala,
• Abstract summary: ATAC-Net is a framework that trains to detect anomalies from a minimal set of known prior anomalies. We substantiate its superiority to some of the current state-of-the-art techniques in a comparable setting.
• Score: 1.024113475677323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The application of deep learning in visual anomaly detection has gained widespread popularity due to its potential use in quality control and manufacturing. Current standard methods are Unsupervised, where a clean dataset is utilised to detect deviations and flag anomalies during testing. However, incorporating a few samples when the type of anomalies is known beforehand can significantly enhance performance. Thus, we propose ATAC-Net, a framework that trains to detect anomalies from a minimal set of known prior anomalies. Furthermore, we introduce attention-guided cropping, which provides a closer view of suspect regions during the training phase. Our framework is a reliable and easy-to-understand system for detecting anomalies, and we substantiate its superiority to some of the current state-of-the-art techniques in a comparable setting.

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