论文标题
签名和对数签名，用于研究用gan产生的经验分布
Signature and Log-signature for the Study of Empirical Distributions Generated with GANs
论文作者
论文摘要
在本文中，我们提出了最近开发的签名变换的使用，以衡量图像分布之间的相似性并提供详细的相识和广泛的评估。我们是第一个先驱RMSE和MAE签名的人，也是对数签名作为测量GAN收敛的替代方法，这是经过广泛研究的问题。我们也是基于统计数据引入分析措施的先驱，以研究既高效又有效的GAN样品分布的拟合度。当前的GAN措施涉及通常在GPU进行的大量计算，并且非常耗时。相比之下，我们将计算时间缩短为秒和计算的顺序，并在CPU上实现相同的善良水平。最后，在这种情况下是新颖的PCA自适应T-SNE方法，也提出了用于数据可视化的。
In this paper, we bring forward the use of the recently developed Signature Transform as a way to measure the similarity between image distributions and provide detailed acquaintance and extensive evaluations. We are the first to pioneer RMSE and MAE Signature, along with log-signature as an alternative to measure GAN convergence, a problem that has been extensively studied. We are also forerunners to introduce analytical measures based on statistics to study the goodness of fit of the GAN sample distribution that are both efficient and effective. Current GAN measures involve lots of computation normally done at the GPU and are very time consuming. In contrast, we diminish the computation time to the order of seconds and computation is done at the CPU achieving the same level of goodness. Lastly, a PCA adaptive t-SNE approach, which is novel in this context, is also proposed for data visualization.
