  Generative feature matching network (GFMN) is an approach for training
implicit generative models for images by performing moment matching on features
from pre-trained neural networks. In this paper, we present new GFMN
formulations that are effective for sequential data. Our experimental results
show the effectiveness of the proposed method, SeqGFMN, for three distinct
generation tasks in English: unconditional text generation, class-conditional
text generation, and unsupervised text style transfer. SeqGFMN is stable to
train and outperforms various adversarial approaches for text generation and
text style transfer.
