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.

翻译：在异常检测领域，异常样本的稀缺性使得当前研究重点转向无监督异常检测。然而，这些无监督方法虽具便利性，却忽略了异常样本中蕴含的关键先验信息。此外，在众多深度学习方法中，有监督方法通常表现优于无监督方法。基于上述原因，我们提出一种结合对比学习与2D-Flow的自监督异常检测方法，以实现更精确的检测结果和更快的推理过程。一方面，我们引入一种新颖的异常合成方法，依据真实工业场景生成异常样本及其代理标注。另一方面，在获取大量异常样本后，我们通过融入对比学习来增强2D-Flow框架，利用多样化代理任务对网络进行微调。该方法使网络能够从自生成标签中学习更精确的映射关系，同时保留2D-Flow的轻量化特性。与主流无监督方法相比，我们的自监督方法展现出更高的检测精度、更少的额外模型参数以及更快的推理速度。此外，整个训练和推理过程均为端到端。我们的方法在MVTecAD数据集上实现了99.6%的图像级AUROC，在BTAD数据集上实现了96.8%的图像级AUROC，刷新了最先进性能。