论文标题
在归一化流中有原则的插值
Principled Interpolation in Normalizing Flows
论文作者
论文摘要
基于归一化流的生成模型在使用较简单的数据分布建模复杂的数据分布方面非常成功。但是，直接线性插值显示出意外的副作用，因为插值路径位于观察到样品的区域之外。这是由高斯基本分布的标准选择引起的，可以在插值样品的规范中看到它们，因为它们在数据歧管之外。该观察结果表明，改变插值的方式通常应该导致更好的插值，但是尚不清楚如何以明确的方式做到这一点。在本文中，我们通过强制执行特定的歧管，然后更改基本分布来解决此问题，以允许有原则的插值方式。具体而言，我们分别将Dirichlet和Von Mises-Fisher基础分布分别用于概率和Hypersphere。我们的实验结果表明，在每个维度，FRéchet成立距离（FID）和内核成立距离（KID）得分方面的表现出色，以进行插值，同时保持生成性能。
Generative models based on normalizing flows are very successful in modeling complex data distributions using simpler ones. However, straightforward linear interpolations show unexpected side effects, as interpolation paths lie outside the area where samples are observed. This is caused by the standard choice of Gaussian base distributions and can be seen in the norms of the interpolated samples as they are outside the data manifold. This observation suggests that changing the way of interpolating should generally result in better interpolations, but it is not clear how to do that in an unambiguous way. In this paper, we solve this issue by enforcing a specific manifold and, hence, change the base distribution, to allow for a principled way of interpolation. Specifically, we use the Dirichlet and von Mises-Fisher base distributions on the probability simplex and the hypersphere, respectively. Our experimental results show superior performance in terms of bits per dimension, Fréchet Inception Distance (FID), and Kernel Inception Distance (KID) scores for interpolation, while maintaining the generative performance.
