309 citations · 493 across the 39 of their papers we have counts for
8 papers · 1 filter
Frequency-Weighted Robust Tensor Principal Component Analysis
Shenghan Wang, Yipeng Liu, Lanlan Feng +1
Robust tensor principal component analysis (RTPCA) can separate the low-rank component and sparse component from multidimensional data, which has been used successfully in several…
Image Ordinal Classification and Understanding: Grid Dropout with Masking Label
Chao Zhang, Ce Zhu, Jimin Xiao +2
Image ordinal classification refers to predicting a discrete target value which carries ordering correlation among image categories. The limited size of labeled ordinal data render…
Towards thinner convolutional neural networks through Gradually Global Pruning
Zhengtao Wang, Ce Zhu, Zhiqiang Xia +2
Deep network pruning is an effective method to reduce the storage and computation cost of deep neural networks when applying them to resource-limited devices. Among many pruning gr…
Attribute-controlled face photo synthesis from simple line drawing
Qi Guo, Ce Zhu, Zhiqiang Xia +2
Face photo synthesis from simple line drawing is a one-to-many task as simple line drawing merely contains the contour of human face. Previous exemplar-based methods are over-depen…
Iterative Block Tensor Singular Value Thresholding for Extraction of Low Rank Component of Image Data
Longxi Chen, Yipeng Liu, Ce Zhu
Tensor principal component analysis (TPCA) is a multi-linear extension of principal component analysis which converts a set of correlated measurements into several principal compon…
Every Filter Extracts A Specific Texture In Convolutional Neural Networks
Zhiqiang Xia, Ce Zhu, Zhengtao Wang +2
Many works have concentrated on visualizing and understanding the inner mechanism of convolutional neural networks (CNNs) by generating images that activate some specific neurons,…