Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
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, classconditional text generation, and unsupervised text style transfer. SeqGFMN is stable to train and outperforms various adversarial approaches for text generation and text style transfer.
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010
Michael Glass, Alfio Gliozzo, et al.
ACL 2020