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arXiv · 2001.06591

Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection

Ziyi Yang·Iman Soltani Bozchalooi·Eric Darve
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Abstract

In this paper, we investigate algorithms for anomaly detection. Previous anomaly detection methods focus on modeling the distribution of non-anomalous data provided during training. However, this does not necessarily ensure the correct detection of anomalous data. We propose a new Regularized Cycle Consistent Generative Adversarial Network (RCGAN) in which deep neural networks are adversarially trained to better recognize anomalous samples. This approach is based on leveraging a penalty distribution with a new definition of the loss function and novel use of discriminator networks. It is based on a solid mathematical foundation, and proofs show that our approach has stronger guarantees for detecting anomalous examples compared to the current state-of-the-art. Experimental results on both real-world and synthetic data show that our model leads to significant and consistent improvements on previous anomaly detection benchmarks. Notably, RCGAN improves on the state-of-the-art on the KDDCUP, Arrhythmia, Thyroid, Musk and CIFAR10 datasets.

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Ziyi Yang, Iman Soltani Bozchalooi, Eric Darve. 2020-01-18. Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection. https://arxiv.org/abs/2001.06591

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