ATAC-Net: Zoomed view works better for Anomaly Detection
This work addresses anomaly detection for quality control and manufacturing, offering an incremental improvement by incorporating prior anomaly samples.
The paper tackles the problem of visual anomaly detection by proposing ATAC-Net, a framework that uses a minimal set of known prior anomalies and attention-guided cropping to improve performance, showing superiority over current state-of-the-art techniques.
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.