论文标题
Algan：通过潜在变量生成伪异常数据的异常检测
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables
论文作者
论文摘要
在许多异常检测任务中，很少出现异常数据并且很难收集，仅使用正常数据训练很重要。尽管可以使用先验知识手动创建异常数据，但它们可能会受到用户偏见的影响。在本文中，我们提出了一个异常的潜在变量生成对抗网络（ALGAN），其中GAN发电机会产生伪反应数据以及伪造的正常数据，而识别器则经过训练以区分正常和伪摩态数据。这与标准GAN歧视器不同，该标准GAN歧视器专门针对两个类似的类别进行分类。培训数据集仅包含正常数据；潜在变量以异常状态引入，并输入到发电机中以产生不同的伪anmalos数据。我们将Algan的性能与MVTEC-AD，磁化图缺陷和COIL-100数据集的其他现有方法进行了比较。实验结果表明，Algan表现出与最先进方法相当的AUROC，同时实现了更快的预测时间。
In many anomaly detection tasks, where anomalous data rarely appear and are difficult to collect, training using only normal data is important. Although it is possible to manually create anomalous data using prior knowledge, they may be subject to user bias. In this paper, we propose an Anomalous Latent variable Generative Adversarial Network (ALGAN) in which the GAN generator produces pseudo-anomalous data as well as fake-normal data, whereas the discriminator is trained to distinguish between normal and pseudo-anomalous data. This differs from the standard GAN discriminator, which specializes in classifying two similar classes. The training dataset contains only normal data; the latent variables are introduced in anomalous states and are input into the generator to produce diverse pseudo-anomalous data. We compared the performance of ALGAN with other existing methods on the MVTec-AD, Magnetic Tile Defects, and COIL-100 datasets. The experimental results showed that ALGAN exhibited an AUROC comparable to those of state-of-the-art methods while achieving a much faster prediction time.
