Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets. Previous work primarily focused on data augmentation for semi-supervised and supervised tasks. In this paper, we instead focus on unsupervised anomaly detection and propose a novel generative data augmentation framework optimized for this task. By using a GAN variant known as the adversarial autoencoder (AAE), we impose a distribution on the latent space of the dataset and systematically sample the latent space to generate artificial samples. To the best of our knowledge, our method is the first data augmentation technique focused on improving performance in unsupervised anomaly detection. We validate our method by demonstrating consistent improvements across several real-world datasets 1 .
Lim et al. first put forward a data augmentation technique focused on improving performance in unsupervised anomaly detection based on GAN REF .
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN
2018 IEEE International Conference on Data Mining (ICDM)
2018 IEEE International Conference on Data Mining (ICDM)
Mathematics Computer Science