Unsupervised Anomaly Detection with a GAN Augmented Autoencoder

Laya Rafiee, Thomas Fevens

Abstract: Anomaly detection is a task of identifying samples that differ from the training data distribution. There are several studies that employ generative adversarial networks (GANs) as the main tool to detect anomalies using the rich contextual information that GANs provide. We propose an unsupervised GAN-based model combined with an autoencoder to detect the anomalies. Then we use the latent information obtained from the autoencoder, the internal representation of the discriminator, and visual information of the generator to assign an anomaly score to samples. This anomaly score is used to discriminate anomalous samples from normal samples. The model was evaluated on benchmark datasets such as MNIST and CIFAR10 plus on a specific domain of medical images of a public Leukemia dataset. The model achieved state-of-the-art performance in comparison with its counterparts in almost all of the experiments.