Generally, current image manipulation detection models are simply built ...
Implicit equilibrium models, i.e., deep neural networks (DNNs) defined b...
Recently, <cit.> studied the rather challenging problem of
time series f...
While successful in many fields, deep neural networks (DNNs) still suffe...
In this paper, we investigate the robust dictionary learning (DL) to dis...
For subspace recovery, most existing low-rank representation (LRR) model...
This paper is about recovering the unseen future data from a given seque...
Concept Factorization (CF) and its variants may produce inaccurate
repre...
In this paper, we extend the popular dictionary pair learning (DPL) into...
Kernel methods have been successfully applied to the areas of pattern
re...
Constrained Concept Factorization (CCF) yields the enhanced representati...
We propose a novel structured discriminative block-diagonal dictionary
l...
We investigate the high-dimensional data clustering problem by proposing...
Recently, a number of learning-based optimization methods that combine
d...
In this paper, we study the problem of matrix recovery, which aims to re...
Prevalent matrix completion theories reply on an assumption that the
loc...
Over the past several decades, subspace clustering has been receiving
in...
As a fundamental technique that concerns several vision tasks such as im...
In this paper, we utilize structured learning to simultaneously address ...
In this paper, we study the problem of recovering a sharp version of a g...
In this work, we address the following matrix recovery problem: suppose ...
It is an efficient and effective strategy to utilize the nuclear norm
ap...
In this work we address the subspace recovery problem. Given a set of da...