activity
20192021
most citedQuantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction

6 citations · 12 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV2021

CompConv: A Compact Convolution Module for Efficient Feature Learning

Chen Zhang, Yinghao Xu, Yujun Shen

Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks but rely on tremendous computational cost. To solve this problem, existing ap…

cs.CV20216 cited

Decorating Your Own Bedroom: Locally Controlling Image Generation with Generative Adversarial Networks

Chen Zhang, Yinghao Xu, Yujun Shen

Generative Adversarial Networks (GANs) have made great success in synthesizing high-quality images. However, how to steer the generation process of a well-trained GAN model and cus…

cs.LG2021

Multi-view Clustering via Deep Matrix Factorization and Partition Alignment

Chen Zhang, Siwei Wang, Jiyuan Liu +5

Multi-view clustering (MVC) has been extensively studied to collect multiple source information in recent years. One typical type of MVC methods is based on matrix factorization to…

cs.LG2021

Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement

Chen Zhang, Siwei Wang, Wenxuan Tu +4

Multi-view clustering is an important yet challenging task in machine learning and data mining community. One popular strategy for multi-view clustering is matrix factorization whi…

cs.LG2021

iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression

Shifeng Zhang, Chen Zhang, Ning Kang +1

It is nontrivial to store rapidly growing big data nowadays, which demands high-performance lossless compression techniques. Likelihood-based generative models have witnessed their…

cs.CV20206 cited

Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction

Riccardo Barbano, Željko Kereta, Chen Zhang +3

Image reconstruction methods based on deep neural networks have shown outstanding performance, equalling or exceeding the state-of-the-art results of conventional approaches, but o…