@inproceedings{yang2019regularized,
author = {Yang, Ziyi and Bozchalooi, Iman Soltani and Darve, Eric},
title = {Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection.},
booktitle = {2020 European Conference on Artificial Intelligence},
year = {2019},
month = {December},
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.},
publisher = {Association for Computational Linguistics},
url = {http://approjects.co.za/?big=en-us/research/publication/regularized-cycle-consistent-generative-adversarial-network-for-anomaly-detection-2/},
pages = {1618-1625},
}