activity
20192021
most citedReal-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning

13 citations · 18 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20211 cited

Off-policy Imitation Learning from Visual Inputs

Zhihao Cheng, Li Shen, Dacheng Tao

Recently, various successful applications utilizing expert states in imitation learning (IL) have been witnessed. However, another IL setting -- IL from visual inputs (ILfVI), whic…

cs.CV20213 cited

End-to-End Adaptive Monte Carlo Denoising and Super-Resolution

Xinyue Wei, Haozhi Huang, Yujin Shi +3

The classic Monte Carlo path tracing can achieve high quality rendering at the cost of heavy computation. Recent works make use of deep neural networks to accelerate this process,…

cs.CV2020

Adaptive Compact Attention For Few-shot Video-to-video Translation

Risheng Huang, Li Shen, Xuan Wang +2

This paper proposes an adaptive compact attention model for few-shot video-to-video translation. Existing works in this domain only use features from pixel-wise attention without c…

cs.CV202013 cited

Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning

Jie An, Tao Li, Haozhi Huang +6

Extracting effective deep features to represent content and style information is the key to universal style transfer. Most existing algorithms use VGG19 as the feature extractor, w…

cs.LG20191 cited

Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning

Yingru Liu, Xuewen Yang, Dongliang Xie +4

Multi-task learning (MTL) is a common paradigm that seeks to improve the generalization performance of task learning by training related tasks simultaneously. However, it is still…