Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization

In the machine learning domain, research on anomaly detection and localization within image data has garnered significant attention, particularly in practical applications such as industrial defect detection. While existing approaches predominantly rely on Convolutional Neural Networks (CNN) as their backbone network, we propose an innovative method based on the Transformer backbone network. Our approach employs a two-stage incremental learning strategy. In the first stage, we train a Masked Autoencoder (MAE) model exclusively on normal images. Subsequently, in the second stage, we implement pixel-level data augmentation techniques to generate corrupted normal images and their corresponding pixel labels. This process enables the model to learn how to repair corrupted regions and classify the state of each pixel. Ultimately, the model produces a pixel reconstruction error matrix and a pixel anomaly probability matrix, which are combined to create an anomaly scoring matrix that effectively identifies abnormal regions. When compared to several state-of-the-art CNN-based techniques, our method demonstrates superior performance on the MVTec AD dataset, achieving an impressive 97.6% AUC.