185 citations · 1.5k across the 88 of their papers we have counts for
12 papers · 1 filter
Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions
Zhenyi Wang, Ping Yu, Yang Zhao +4
Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence…
KernelNet: A Data-Dependent Kernel Parameterization for Deep Generative Modeling
Yufan Zhou, Changyou Chen, Jinhui Xu
Learning with kernels is an important concept in machine learning. Standard approaches for kernel methods often use predefined kernels that require careful selection of hyperparame…
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-Resolution
Yitong Yan, Chuangchuang Liu, Changyou Chen +3
The traditional super-resolution methods that aim to minimize the mean square error usually produce the images with over-smoothed and blurry edges, due to the lose of high-frequenc…
Document Hashing with Mixture-Prior Generative Models
Wei Dong, Qinliang Su, Dinghan Shen +1
Hashing is promising for large-scale information retrieval tasks thanks to the efficiency of distance evaluation between binary codes. Generative hashing is often used to generate…
Implicit Deep Latent Variable Models for Text Generation
Le Fang, Chunyuan Li, Jianfeng Gao +2
Deep latent variable models (LVM) such as variational auto-encoder (VAE) have recently played an important role in text generation. One key factor is the exploitation of smooth lat…
Bayesian Uncertainty Matching for Unsupervised Domain Adaptation
Jun Wen, Nenggan Zheng, Junsong Yuan +2
Domain adaptation is an important technique to alleviate performance degradation caused by domain shift, e.g., when training and test data come from different domains. Most existin…