论文标题
在多元时间序列下使用自身的视觉评估生成对抗网络
Visually Evaluating Generative Adversarial Networks Using Itself under Multivariate Time Series
论文作者
论文摘要
在视觉上评估生成的多元时间序列（MT）的优点很难实现，尤其是在生成模型是生成性对抗网络（GAN）的情况下。我们提出了一个名为高斯甘斯（Gaussian Gans）的一般框架，以视觉评估MTS生成任务下的使用自身。首先，我们试图通过明确重建gan的体系结构来找到多元Kolmogorov Smirnov（MKS）测试中的转换函数。其次，我们对转化的MST进行了正态性测试，在该测试中，高斯gans在MKS测试中充当转换函数。为了简化正态性测试，使用Chi Square分布提出了有效的可视化。在实验中，我们使用UNIMIB数据集并提供经验证据，表明使用高斯gans和Chi sqaure可视化的正态性测试是有效且可信的。
Visually evaluating the goodness of generated Multivariate Time Series (MTS) are difficult to implement, especially in the case that the generative model is Generative Adversarial Networks (GANs). We present a general framework named Gaussian GANs to visually evaluate GANs using itself under the MTS generation task. Firstly, we attempt to find the transformation function in the multivariate Kolmogorov Smirnov (MKS) test by explicitly reconstructing the architecture of GANs. Secondly, we conduct the normality test of transformed MST where the Gaussian GANs serves as the transformation function in the MKS test. In order to simplify the normality test, an efficient visualization is proposed using the chi square distribution. In the experiment, we use the UniMiB dataset and provide empirical evidence showing that the normality test using Gaussian GANs and chi sqaure visualization is effective and credible.
