论文标题
正规周期一致的生成对抗网络用于异常检测
Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection
论文作者
论文摘要
在本文中，我们研究了用于异常检测的算法。以前的异常检测方法着重于建模训练过程中提供的非反对数据的分布。但是，这不一定确保正确检测异常数据。我们提出了一个新的正规周期一致的生成对抗网络（RCGAN），其中深层神经网络经过对抗训练以更好地识别异常样本。这种方法基于利用损失函数的新定义和歧视网络的新颖使用的新定义。它基于一个坚实的数学基础，证明我们的方法具有更强的保证，可以与当前的最新作品相比检测异常示例。对现实世界和合成数据的实验结果表明，我们的模型会导致对先前的异常检测基准的显着和一致的改进。值得注意的是，RCGAN在KDDCUP，心律失常，甲状腺，Musk和Cifar10数据集上的最新技术改进。
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.
