ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables

In many anomaly detection tasks, where anomalous data rarely appear and are difficult to collect, training with 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 as anomalous states are introduced in the latent variable and input them into the generator to produce diverse pseudo-anomalous data. We compared the performance of ALGAN with other existing methods using the MVTec-AD, Magnetic Tile Defects, and COIL-100 datasets. The experimental results showed that the proposed ALGAN exhibited an AUROC comparable to state-of-the-art methods while achieving a much faster prediction time.