Developing deep generative models that flexibly incorporate diverse measures of probability distance is an important area of research. Here we develop an unified mathematical framework of f-divergence generative model, f-GM, that incorporates both VAE and f-GAN, and enables tractable learning with general f-divergences. f-GM allows the experimenter to flexibly design the f-divergence function without changing the structure of the networks or the learning procedure. f-GM jointly models three components: a generator, a inference network and a density estimator. Therefore it simultaneously enables sampling, posterior inference of the latent variable as well as evaluation of the likelihood of an arbitrary datum. f-GM belongs to the class of encoder-decoder GANs: our density estimator can be interpreted as playing the role of a discriminator between samples in the joint space of latent code and observed space. We prove that f-GM naturally simplifies to the standard VAE and to f-GAN as special cases, and illustrates the connections between different encoder-decoder GAN architectures. f-GM is compatible with general network architecture and optimizer. We leverage it to experimentally explore the effects -- e.g. mode collapse and image sharpness -- of different choices of f-divergence.
翻译:开发深度基因模型,灵活地纳入各种概率距离的计量方法,这是一个重要的研究领域。在这里,我们开发了一个包含VAE和f-GAN的F-GM组合基因模型(f-GM)的统一数学框架,将VAE和f-GAN结合起来,并能够用一般的f-divegence进行可移植的学习。 f-GM允许实验者在不改变网络结构或学习程序的情况下灵活设计F-Diverence功能。 f-GM联合模型的三个组成部分:生成器、推断网络和密度估计器。因此,它同时能够取样、潜在变量的后推推推力以及评估任意达图的可能性。 f-GM属于编码变异器GAN的类别:我们的密度估计器可以被解释为在潜在代码和观测空间的联合空间的样本之间扮演歧视器的作用。 我们证明,F-GM自然地将标准VAE和F-GAN作为特例,并展示了不同摄像分解变变变变变变变变变变变变变变变变变变变变形变形变形变形变形变形变形变形变形的GAN结构。我们的GAN模型和GAN结构与GAN的模型与GAN最均化变形变形变形变形变形变形变形变形变形变形的图像结构。