Publications [#342834] of Lawrence Carin

Papers Published

1. Chen, L; Dai, S; Tao, C; Shen, D; Gan, Z; Zhang, H; Zhang, Y; Carin, L, Adversarial text generation via feature-mover's distance, Advances in Neural Information Processing Systems, vol. 2018-December (January, 2018), pp. 4666-4677
   (last updated on 2024/12/31)

   Abstract:
   Generative adversarial networks (GANs) have achieved significant success in generating real-valued data. However, the discrete nature of text hinders the application of GAN to text-generation tasks. Instead of using the standard GAN objective, we propose to improve text-generation GAN via a novel approach inspired by optimal transport. Specifically, we consider matching the latent feature distributions of real and synthetic sentences using a novel metric, termed the feature-mover's distance (FMD). This formulation leads to a highly discriminative critic and easy-to-optimize objective, overcoming the mode-collapsing and brittle-training problems in existing methods. Extensive experiments are conducted on a variety of tasks to evaluate the proposed model empirically, including unconditional text generation, style transfer from non-parallel text, and unsupervised cipher cracking. The proposed model yields superior performance, demonstrating wide applicability and effectiveness.