Publications [#338659] of Lawrence Carin

Papers Published

1. Zhang, Y; Gan, Z; Fan, K; Chen, Z; Henao, R; Shen, D; Carin, L, Adversarial feature matching for text generation, 34th International Conference on Machine Learning, ICML 2017, vol. 8 (January, 2017), pp. 6093-6102
   (last updated on 2024/12/31)

   Abstract:
   The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of GAN to text. We propose a framework for generating realistic text via adversarial training. We employ a long short-term memory network as generator, and a convolutional network as discriminator. Instead of using the standard objective of GAN, we propose matching the high-dimensional latent feature distributions of real and synthetic sentences, via a kernelized discrepancy metric. This eases adversarial training by alleviating the mode-collapsing problem. Our experiments show superior performance in quantitative evaluation, and demonstrate that our model can generate realistic-looking sentences.