activity
20172020
most citedCLUB: A Contrastive Log-ratio Upper Bound of Mutual Information

58 citations · 75 across the 3 of their papers we have counts for

collaborators

7 papers

cs.LG202058 cited

CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information

Pengyu Cheng, Weituo Hao, Shuyang Dai +3

Mutual information (MI) minimization has gained considerable interests in various machine learning tasks. However, estimating and minimizing MI in high-dimensional spaces remains a…

cs.LG2020

Bridging Maximum Likelihood and Adversarial Learning via -Divergence

Miaoyun Zhao, Yulai Cong, Shuyang Dai +1

Maximum likelihood (ML) and adversarial learning are two popular approaches for training generative models, and from many perspectives these techniques are complementary. ML learni…

cs.LG2019

Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation

Shuyang Dai, Yu Cheng, Yizhe Zhang +3

Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order…

cs.CV2019

Adaptation Across Extreme Variations using Unlabeled Domain Bridges

Shuyang Dai, Kihyuk Sohn, Yi-Hsuan Tsai +2

We tackle an unsupervised domain adaptation problem for which the domain discrepancy between labeled source and unlabeled target domains is large, due to many factors of inter and…

cs.CL2018

Adversarial Text Generation via Feature-Mover's Distance

Liqun Chen, Shuyang Dai, Chenyang Tao +5

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…

cs.LG2018

JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets

Yunchen Pu, Shuyang Dai, Zhe Gan +5

A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed m…