  In the anomaly detection field, the scarcity of anomalous samples has
directed the current research emphasis towards unsupervised anomaly detection.
While these unsupervised anomaly detection methods offer convenience, they also
overlook the crucial prior information embedded within anomalous samples.
Moreover, among numerous deep learning methods, supervised methods generally
exhibit superior performance compared to unsupervised methods. Considering the
reasons mentioned above, we propose a self-supervised anomaly detection
approach that combines contrastive learning with 2D-Flow to achieve more
precise detection outcomes and expedited inference processes. On one hand, we
introduce a novel approach to anomaly synthesis, yielding anomalous samples in
accordance with authentic industrial scenarios, alongside their surrogate
annotations. On the other hand, having obtained a substantial number of
anomalous samples, we enhance the 2D-Flow framework by incorporating
contrastive learning, leveraging diverse proxy tasks to fine-tune the network.
Our approach enables the network to learn more precise mapping relationships
from self-generated labels while retaining the lightweight characteristics of
the 2D-Flow. Compared to mainstream unsupervised approaches, our
self-supervised method demonstrates superior detection accuracy, fewer
additional model parameters, and faster inference speed. Furthermore, the
entire training and inference process is end-to-end. Our approach showcases new
state-of-the-art results, achieving a performance of 99.6\% in image-level
AUROC on the MVTecAD dataset and 96.8\% in image-level AUROC on the BTAD
dataset.
