论文标题
survae流：弥合VAE和流之间的缝隙的过渡
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
论文作者
论文摘要
标准化流量和变异自动编码器是强大的生成模型，可以代表复杂的密度函数。但是，它们都对模型施加限制：归一化流对建模密度进行模型，而VAE则学习不可依赖的随机转换，因此通常不提供边际可能性的可拖动估计值。在本文中，我们介绍了Survae Flow：一个模块化的框架，包括构成VAE和归一化流量的可组合变换。 Survae Flow桥接了归一化流与VAE之间的差距，其中有圆形的转换，其中转化是一个方向的决定性的，从而允许精确的可能性计算，并在相反的方向上进行随机性 - 因此在相应的可能性上提供了下限。我们表明，最近提出的几种方法，包括取消和增强的归一化流，可以表示为Survae Flow。最后，我们引入了常见的操作，例如最大值，绝对值，排序和随机置换，作为Survae Flow中的可综合层。
Normalizing flows and variational autoencoders are powerful generative models that can represent complicated density functions. However, they both impose constraints on the models: Normalizing flows use bijective transformations to model densities whereas VAEs learn stochastic transformations that are non-invertible and thus typically do not provide tractable estimates of the marginal likelihood. In this paper, we introduce SurVAE Flows: A modular framework of composable transformations that encompasses VAEs and normalizing flows. SurVAE Flows bridge the gap between normalizing flows and VAEs with surjective transformations, wherein the transformations are deterministic in one direction -- thereby allowing exact likelihood computation, and stochastic in the reverse direction -- hence providing a lower bound on the corresponding likelihood. We show that several recently proposed methods, including dequantization and augmented normalizing flows, can be expressed as SurVAE Flows. Finally, we introduce common operations such as the max value, the absolute value, sorting and stochastic permutation as composable layers in SurVAE Flows.
