DeepAI AI Chat
Log In Sign Up

Deep Generative Learning via Variational Gradient Flow

by   Gao Yuan, et al.
Wuhan University
The Hong Kong University of Science and Technology

We propose a general framework to learn deep generative models via Variational Gradient Flow (VGrow) on probability spaces. The evolving distribution that asymptotically converges to the target distribution is governed by a vector field, which is the negative gradient of the first variation of the f-divergence between them. We prove that the evolving distribution coincides with the pushforward distribution through the infinitesimal time composition of residual maps that are perturbations of the identity map along the vector field. The vector field depends on the density ratio of the pushforward distribution and the target distribution, which can be consistently learned from a binary classification problem. Connections of our proposed VGrow method with other popular methods, such as VAE, GAN and flow-based methods, have been established in this framework, gaining new insights of deep generative learning. We also evaluated several commonly used divergences, including Kullback-Leibler, Jensen-Shannon, Jeffrey divergences as well as our newly discovered `logD' divergence which serves as the objective function of the logD-trick GAN. Experimental results on benchmark datasets demonstrate that VGrow can generate high-fidelity images in a stable and efficient manner, achieving competitive performance with state-of-the-art GANs.


page 19

page 20

page 21

page 22


Refining Deep Generative Models via Wasserstein Gradient Flows

Deep generative modeling has seen impressive advances in recent years, t...

Out-of-Distribution Detection with Distance Guarantee in Deep Generative Models

Recent research has shown that it is challenging to detect out-of-distri...

Continuous-Time Flows for Deep Generative Models

Normalizing flows have been developed recently as a method for drawing s...

KernelNet: A Data-Dependent Kernel Parameterization for Deep Generative Modeling

Learning with kernels is an often resorted tool in modern machine learni...

Deep Generative Learning via Schrödinger Bridge

We propose to learn a generative model via entropy interpolation with a ...

Active Divergence with Generative Deep Learning – A Survey and Taxonomy

Generative deep learning systems offer powerful tools for artefact gener...

Code Repositories


A tensorflow implementation of VGrow by using progressive growing method.

view repo


A tensorflow implementation of VGrow by using progressive growing method.

view repo