3.7k citations · 4.1k across the 25 of their papers we have counts for
26 papers
Auto-Focus Contrastive Learning for Image Manipulation Detection
Wenyan Pan, Zhili Zhou, Guangcan Liu +3
Generally, current image manipulation detection models are simply built on manipulation traces. However, we argue that those models achieve sub-optimal detection performance as it…
Optimization Induced Equilibrium Networks
Xingyu Xie, Qiuhao Wang, Zenan Ling +4
Implicit equilibrium models, i.e., deep neural networks (DNNs) defined by implicit equations, have been becoming more and more attractive recently. In this paper, we investigate an…
Time Series Forecasting via Learning Convolutionally Low-Rank Models
Guangcan Liu
Recently, Liu and Zhang studied the rather challenging problem of time series forecasting from the perspective of compressed sensing. They proposed a no-learning method, named Conv…
Maximum-and-Concatenation Networks
Xingyu Xie, Hao Kong, Jianlong Wu +3
While successful in many fields, deep neural networks (DNNs) still suffer from some open problems such as bad local minima and unsatisfactory generalization performance. In this wo…
Multilayer Collaborative Low-Rank Coding Network for Robust Deep Subspace Discovery
Xianzhen Li, Zhao Zhang, Yang Wang +3
For subspace recovery, most existing low-rank representation (LRR) models performs in the original space in single-layer mode. As such, the deep hierarchical information cannot be…
Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space
Jiahuan Ren, Zhao Zhang, Sheng Li +4
In this paper, we investigate the robust dictionary learning (DL) to discover the hybrid salient low-rank and sparse representation in a factorized compressed space. A Joint Robust…