Unsupervised anomaly detection using inverse generative adversarial networks

Feng Xiao, Jianfeng Zhou, Kunpeng Han, Haoyuan Hu, Jicong Fan

Abstract: Unsupervised anomaly detection (UAD) holds considerable promise for a myriad of real-world applications related to information sciences. Recently, generative models, such as Generative Adversarial Networks (GANs), have shown their potential in addressing UAD tasks. Despite their potential, current GAN-based UAD methods frequently employ intricate network architectures. Some even necessitate solving an iterative optimization problem for each test sample, making them both time-consuming and impractical for large-scale datasets or time-sensitive industrial applications. In this work, we introduce a novel generative adversarial learning method for UAD. Diverging from existing GAN-based methods, our method incorporates a distribution converter and a distribution discriminator. The converter transforms normal data into a compact target distribution such as a truncated Gaussian while the discriminator endeavors to distinguish the transformed data from the data in the target distribution. After an adversarial training process, the learned converter becomes proficient in transforming normal data into the target distribution but cannot accurately transform anomalous samples to the target distribution, which can be easily identified by the strong discriminator. Extensive experiments on nine publicly available anomaly detection benchmark datasets and a real-world, large-scale logistics dataset demonstrate that our method achieves superior detection accuracy and lower inference time than many strong baselines. Our code is publicly available in https://github.com/xiaofeng-github/IGAN.