ATAC Net

ATAC-Net, and related networks, represent a class of deep learning models designed for anomaly detection in visual data, particularly focusing on improving efficiency and accuracy with limited labeled anomaly examples. Current research emphasizes attention mechanisms to guide the network towards relevant image regions and explores various network architectures, including encoder-decoder structures and attention-based modules, to enhance feature extraction and anomaly classification. These advancements hold significant potential for improving quality control in manufacturing and other applications requiring real-time anomaly detection from visual data.