Generating realistic sequences is a central task in many machine learning applications. There has been considerable recent progress on building deep generative models for sequence generation tasks. However, the issue of mode-collapsing remains a main issue for the current models. In this paper we propose a GAN-based generic framework to address the problem of mode-collapse in a principled approach. We change the standard GAN objective to maximize a variational lower-bound of the log-likelihood while minimizing the Jensen-Shanon divergence between data and model distributions. We experiment our model with text generation task and show that it can generate realistic text with high diversity.
@article{arxiv.2104.13488,
title = {Text Generation with Deep Variational GAN},
author = {Mahmoud Hossam and Trung Le and Michael Papasimeon and Viet Huynh and Dinh Phung},
journal= {arXiv preprint arXiv:2104.13488},
year = {2021}
}
Comments
Accepted in the Third Workshop on Bayesian Deep Learning (NIPS / NeurIPS 2018)