In this paper, we integrate VAEs and flow-based generative models
successfully and get f-VAEs. Compared with VAEs, f-VAEs generate more vivid
images, solved the blurred-image problem of VAEs. Compared with flow-based
models such as Glow, f-VAE is more lightweight and converges faster, achieving
the same performance under smaller-size architecture.
Description
[1809.05861] f-VAEs: Improve VAEs with Conditional Flows
%0 Journal Article
%1 su2018fvaes
%A Su, Jianlin
%A Wu, Guang
%D 2018
%K flows generative-models
%T f-VAEs: Improve VAEs with Conditional Flows
%U http://arxiv.org/abs/1809.05861
%X In this paper, we integrate VAEs and flow-based generative models
successfully and get f-VAEs. Compared with VAEs, f-VAEs generate more vivid
images, solved the blurred-image problem of VAEs. Compared with flow-based
models such as Glow, f-VAE is more lightweight and converges faster, achieving
the same performance under smaller-size architecture.
@article{su2018fvaes,
abstract = {In this paper, we integrate VAEs and flow-based generative models
successfully and get f-VAEs. Compared with VAEs, f-VAEs generate more vivid
images, solved the blurred-image problem of VAEs. Compared with flow-based
models such as Glow, f-VAE is more lightweight and converges faster, achieving
the same performance under smaller-size architecture.},
added-at = {2019-12-09T11:33:35.000+0100},
author = {Su, Jianlin and Wu, Guang},
biburl = {https://www.bibsonomy.org/bibtex/20795b32628181f802fc86ceeef11daa1/kirk86},
description = {[1809.05861] f-VAEs: Improve VAEs with Conditional Flows},
interhash = {a63e01ea67dcd29e9b68180922791443},
intrahash = {0795b32628181f802fc86ceeef11daa1},
keywords = {flows generative-models},
note = {cite arxiv:1809.05861},
timestamp = {2019-12-09T11:33:35.000+0100},
title = {f-VAEs: Improve VAEs with Conditional Flows},
url = {http://arxiv.org/abs/1809.05861},
year = 2018
}