Multi-Resolution Continuous Normalizing Flows

06/15/2021
by   Vikram Voleti, et al.
0

Recent work has shown that Neural Ordinary Differential Equations (ODEs) can serve as generative models of images using the perspective of Continuous Normalizing Flows (CNFs). Such models offer exact likelihood calculation, and invertible generation/density estimation. In this work we introduce a Multi-Resolution variant of such models (MRCNF), by characterizing the conditional distribution over the additional information required to generate a fine image that is consistent with the coarse image. We introduce a transformation between resolutions that allows for no change in the log likelihood. We show that this approach yields comparable likelihood values for various image datasets, with improved performance at higher resolutions, with fewer parameters, using only 1 GPU. Further, we examine the out-of-distribution properties of (Multi-Resolution) Continuous Normalizing Flows, and find that they are similar to those of other likelihood-based generative models.

READ FULL TEXT

page 8

page 16

research
10/02/2018

FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

A promising class of generative models maps points from a simple distrib...
research
02/12/2019

MaCow: Masked Convolutional Generative Flow

Flow-based generative models, conceptually attractive due to tractabilit...
research
10/07/2020

Equivariant Normalizing Flows for Point Processes and Sets

A point process describes how random sets of exchangeable points are gen...
research
09/30/2019

Localised Generative Flows

We argue that flow-based density models based on continuous bijections a...
research
12/09/2019

InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers

Continuous Normalizing Flows (CNFs) have emerged as promising deep gener...
research
01/06/2020

Granular Learning with Deep Generative Models using Highly Contaminated Data

An approach to utilize recent advances in deep generative models for ano...
research
05/19/2021

E(n) Equivariant Normalizing Flows for Molecule Generation in 3D

This paper introduces a generative model equivariant to Euclidean symmet...

Please sign up or login with your details

Forgot password? Click here to reset