论文标题
GAN合奏以进行异常检测
GAN Ensemble for Anomaly Detection
论文作者
论文摘要
当作为无监督的学习问题配方时，异常检测通常需要模型来学习正常数据的分布。先前的工作将生成对抗网络（GAN）应用于异常检测任务，并显示了这些模型的良好性能。通过观察到GAN合奏在一代任务中通常超过单个gan的观察，我们建议构建用于异常检测的GAN合奏。在提出的方法中，一组发电机和一组歧视者都经过培训，因此每个发电机都会从多个歧视器中获得反馈，反之亦然。与单个GAN相比，GAN集合可以更好地对正常数据的分布进行建模，从而更好地检测异常。我们对gan和gan集合的理论分析解释了gan歧视剂在异常检测中的作用。在实证研究中，我们评估了从四种类型的基本模型构建的合奏，结果表明，在一系列异常检测任务中，这些集合显然超过了单个模型。
When formulated as an unsupervised learning problem, anomaly detection often requires a model to learn the distribution of normal data. Previous works apply Generative Adversarial Networks (GANs) to anomaly detection tasks and show good performances from these models. Motivated by the observation that GAN ensembles often outperform single GANs in generation tasks, we propose to construct GAN ensembles for anomaly detection. In the proposed method, a group of generators and a group of discriminators are trained together, so every generator gets feedback from multiple discriminators, and vice versa. Compared to a single GAN, a GAN ensemble can better model the distribution of normal data and thus better detect anomalies. Our theoretical analysis of GANs and GAN ensembles explains the role of a GAN discriminator in anomaly detection. In the empirical study, we evaluate ensembles constructed from four types of base models, and the results show that these ensembles clearly outperform single models in a series of tasks of anomaly detection.
