PDF投影与归一化流及SurVAE的比较
A Comparison of PDF Projection with Normalizing Flows and SurVAE
AI总结：
本文对比指出，近期提出的归一化流和SurVAE生成网络方法实际上是对二十多年前已出现且更成熟的PDF投影技术的重新发明。
摘要：
归一化流（NF）近期作为一种通过可组合层构建具有精确似然计算的生成网络的方法而受到关注。然而，NF局限于保维变换。Surjection VAE（SurVAE）被提出以将NF扩展至变维变换。此类网络因其表达力强且可精确训练而备受青睐。我们表明，这些方法实际上是对PDF投影的重新发明，后者出现于二十多年前且已发展得更为成熟。
英文摘要：
Normalizing flows (NF) recently gained attention as a way to construct generative networks with exact likelihood calculation out of composable layers. However, NF is restricted to dimension-preserving transformations. Surjection VAE (SurVAE) has been proposed to extend NF to dimension-altering transformations. Such networks are desirable because they are expressive and can be precisely trained. We show that the approaches are a re-invention of PDF projection, which appeared over twenty years earlier and is much further developed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
