GAN Ensemble for Anomaly Detection

Authors

  • Xu Han Tufts University
  • Xiaohui Chen Tufts University
  • Li-Ping Liu Tufts University

Keywords:

Anomaly/Outlier Detection, Adversarial Learning & Robustness, Ensemble Methods, Representation Learning

Abstract

When formulated as an unsupervised learning problem, anomaly detection often requires a model to learn the distribution of normal data. Previous works modify Generative Adversarial Networks (GANs) by using encoder-decoders as generators and apply them to anomaly detection tasks. Previous studies indicate that GAN ensembles are often more stable than single GANs in image generation tasks. In this work, we propose to construct GAN ensembles for anomaly detection. In the proposed method, a group of generators interact with a group of discriminators, so every generator gets feedback from every discriminator, and vice versa. Compared to a single GAN, an ensemble of GANs can better model the distribution of normal data and thus better detect anomalies. We also make a theoretical analysis of GANs and GAN ensembles in the context of anomaly detection. The empirical study constructs ensembles based on four different types of detecting models, and the results show that the ensemble outperforms the single model for all four model types.

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Published

2021-05-18

How to Cite

Han, X., Chen, X., & Liu, L.-P. (2021). GAN Ensemble for Anomaly Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4090-4097. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16530

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management