论文标题
对密度估计的gan和归一化流的经验比较
An Empirical Comparison of GANs and Normalizing Flows for Density Estimation
论文作者
论文摘要
生成对抗网络（GAN）和正常化流都是密度估计的方法，使用深度神经网络将样本从非信息的先验分布转换为数据分布的近似值。对于通用统计模型，这两者都非常感兴趣，但是两种方法很少被比较以建模非图像数据。与隐式模型计算可能性的可能性的困难使得进行了如此挑战。我们通过考虑几个低维合成数据集来解决这个困难。对GAN体系结构，超参数和培训程序进行了广泛的网格搜索表明，没有GAN能够很好地对我们简单的低维数据进行建模，我们认为这项任务是一种可被认为适合通用统计模型的方法的先决条件。另一方面，在这些任务方面，几个标准化的流程都擅长于这些任务，甚至在Wasserstein距离方面表现出色，这是惠甘（Waserstein）距离的范围（这是惠甘（Wan）单独针对的指标。科学家和其他从业人员应谨慎依靠WGAN进行需要准确估算的应用。
Generative adversarial networks (GANs) and normalizing flows are both approaches to density estimation that use deep neural networks to transform samples from an uninformative prior distribution to an approximation of the data distribution. There is great interest in both for general-purpose statistical modeling, but the two approaches have seldom been compared to each other for modeling non-image data. The difficulty of computing likelihoods with GANs, which are implicit models, makes conducting such a comparison challenging. We work around this difficulty by considering several low-dimensional synthetic datasets. An extensive grid search over GAN architectures, hyperparameters, and training procedures suggests that no GAN is capable of modeling our simple low-dimensional data well, a task we view as a prerequisite for an approach to be considered suitable for general-purpose statistical modeling. Several normalizing flows, on the other hand, excelled at these tasks, even substantially outperforming WGAN in terms of Wasserstein distance -- the metric that WGAN alone targets. Scientists and other practitioners should be wary of relying on WGAN for applications that require accurate density estimation.
