416 citations · 2.7k across the 106 of their papers we have counts for
8 papers · 1 filter
Context-Aware Semantic Inpainting
Haofeng Li, Guanbin Li, Liang Lin +1
Recently image inpainting has witnessed rapid progress due to generative adversarial networks (GAN) that are able to synthesize realistic contents. However, most existing GAN-based…
High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
Xiaoguang Han, Zhen Li, Haibin Huang +2
We propose a data-driven method for recovering miss-ing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structu…
Folding membrane proteins by deep transfer learning
Sheng Wang, Zhen Li, Yizhou Yu +1
Computational elucidation of membrane protein (MP) structures is challenging partially due to lack of sufficient solved structures for homology modeling. Here we describe a high-th…
DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
Xiaoguang Han, Chang Gao, Yizhou Yu
Face modeling has been paid much attention in the field of visual computing. There exist many scenarios, including cartoon characters, avatars for social media, 3D face caricatures…
Neural Style Transfer: A Review
Yongcheng Jing, Yezhou Yang, Zunlei Feng +3
The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style.…
Predicting membrane protein contacts from non-membrane proteins by deep transfer learning
Zhen Li, Sheng Wang, Yizhou Yu +1
Computational prediction of membrane protein (MP) structures is very challenging partially due to lack of sufficient solved structures for homology modeling. Recently direct evolut…
Instance-Level Salient Object Segmentation
Guanbin Li, Yuan Xie, Liang Lin +1
Image saliency detection has recently witnessed rapid progress due to deep convolutional neural networks. However, none of the existing methods is able to identify object instances…
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning
Weifeng Ge, Yizhou Yu
Deep neural networks require a large amount of labeled training data during supervised learning. However, collecting and labeling so much data might be infeasible in many cases. In…