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

Understand

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.