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.

中文翻译：

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用于异常检测的正则化循环一致生成对抗网络

在本文中，我们研究了异常检测算法。以前的异常检测方法侧重于对训练期间提供的非异常数据的分布进行建模。然而，这并不一定能确保异常数据的正确检测。我们提出了一种新的正则化循环一致生成对抗网络 (RCGAN)，其中深度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鉴别器网络的新用途来利用惩罚分布。它基于坚实的数学基础，证据表明，与当前最先进的技术相比，我们的方法在检测异常示例方面具有更强的保证。对真实世界和合成数据的实验结果表明，我们的模型对以前的异常检测基准产生了显着且一致的改进。值得注意的是，RCGAN 改进了 KDDCUP、心律失常、甲状腺、马斯克和 CIFAR10 数据集的最新技术。