Long-Tailed Anomaly Detection with Learnable Class Names for Scalable and Robust Defect Identification

LTAD combines anomaly detection by reconstruction and semantic anomaly detection to detect defects across multiple and long-tailed image classes, without relying on dataset class names. It learns pseudo-class names and uses a VAE-based data augmentation to address the long-tailed distribution of real-world applications.