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.
@article{arxiv.2001.06591,
title = {Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection},
author = {Ziyi Yang and Iman Soltani Bozchalooi and Eric Darve},
journal= {arXiv preprint arXiv:2001.06591},
year = {2020}
}
Comments
the 24th European Conference on Artificial Intelligence (ECAI 2020)