In this study, a new Anomaly Detection (AD) approach for real-world images is proposed. This method leverages the theoretical strengths of unsupervised learning and the data availability of both normal and abnormal classes. The AD is often formulated as an unsupervised task motivated by the frequent imbalanced nature of the datasets, as well as the challenge of capturing the entirety of the abnormal class. Such methods only rely on normal images during training, which are devoted to be reconstructed through an autoencoder architecture for instance. However, the information contained in the abnormal data is also valuable for this reconstruction. Indeed, the model would be able to identify its weaknesses by better learning how to transform an abnormal (or normal) image into a normal (or abnormal) image. Each of these tasks could help the entire model to learn with higher precision than a single normal to normal reconstruction. To address this challenge, the proposed method utilizes Cycle-Generative Adversarial Networks (Cycle-GANs) for abnormal-to-normal translation. To the best of our knowledge, this is the first time that Cycle-GANs have been studied for this purpose. After an input image has been reconstructed by the normal generator, an anomaly score describes the differences between the input and reconstructed images. Based on a threshold set with a business quality constraint, the input image is then flagged as normal or not. The proposed method is evaluated on industrial and medical images, including cases with balanced datasets and others with as few as 30 abnormal images. The results demonstrate accurate performance and good generalization for all kinds of anomalies, specifically for texture-shaped images where the method reaches an average accuracy of 97.2% (85.4% with an additional zero false negative constraint).

翻译：本研究提出了一种面向真实世界图像的新型异常检测方法，该方法融合了无监督学习的理论优势与正常及异常类别的数据可获得性。异常检测通常被构建为无监督任务，这是由于数据集普遍存在的不平衡特性以及捕捉异常类别完整性的挑战。此类方法在训练过程中仅依赖正常图像，例如通过自编码器架构进行重构。然而，异常数据中包含的信息对这一重构过程同样具有重要价值——模型通过更有效地学习如何将异常（或正常）图像转换为正常（或异常）图像，能够识别自身局限性。相较于单一的正常到正常重构模式，这些任务均有助于整体模型实现更高精度的学习。为应对这一挑战，所提方法采用循环生成对抗网络进行异常到正常的转换。据我们所知，这是首次将循环生成对抗网络应用于该场景。当输入图像经正常生成器重构后，通过异常分数表征输入图像与重构图像之间的差异。基于业务质量约束设定的阈值，输入图像将被标记为正常或异常。该方法在工业与医学图像上进行了评估，涵盖数据集平衡的案例及异常图像数量低至30张的案例。实验结果表明，该方法对所有类型的异常均展现出精确的性能与良好的泛化能力，尤其在纹理类图像上实现了97.2%的平均准确率（在附加零假阴性约束条件下为85.4%）。