  In the realm of machine learning, the study of anomaly detection and
localization within image data has gained substantial traction, particularly
for practical applications such as industrial defect detection. While the
majority of existing methods predominantly use Convolutional Neural Networks
(CNN) as their primary network architecture, we introduce a novel approach
based on the Transformer backbone network. Our method employs a two-stage
incremental learning strategy. During the first stage, we train a Masked
Autoencoder (MAE) model solely on normal images. In the subsequent stage, we
apply pixel-level data augmentation techniques to generate corrupted normal
images and their corresponding pixel labels. This process allows the model to
learn how to repair corrupted regions and classify the status of each pixel.
Ultimately, the model generates a pixel reconstruction error matrix and a pixel
anomaly probability matrix. These matrices are then combined to produce an
anomaly scoring matrix that effectively detects abnormal regions. When
benchmarked against several state-of-the-art CNN-based methods, our approach
exhibits superior performance on the MVTec AD dataset, achieving an impressive
97.6% AUC.
