Image denoising using deep CNN with batch renormalization
Deep convolutional neural networks (CNNs) have attracted great attention in the field of
image denoising. However, there are two drawbacks:(1) it is very difficult to train a deeper …
image denoising. However, there are two drawbacks:(1) it is very difficult to train a deeper …
Generalized incomplete multiview clustering with flexible locality structure diffusion
An important underlying assumption that guides the success of the existing multiview
learning algorithms is the full observation of the multiview data. However, such rigorous …
learning algorithms is the full observation of the multiview data. However, such rigorous …
Incomplete multiview spectral clustering with adaptive graph learning
In this paper, we propose a general framework for incomplete multiview clustering. The
proposed method is the first work that exploits the graph learning and spectral clustering …
proposed method is the first work that exploits the graph learning and spectral clustering …
CMC: a consensus multi-view clustering model for predicting Alzheimer's disease progression
Abstract Machine learning has been used in the past for the auxiliary diagnosis of
Alzheimer's Disease (AD). However, most existing technologies only explore single-view …
Alzheimer's Disease (AD). However, most existing technologies only explore single-view …
TUSR-Net: triple unfolding single image dehazing with self-regularization and dual feature to pixel attention
Single image dehazing is a challenging and ill-posed problem due to severe information
degeneration of images captured in hazy conditions. Remarkable progresses have been …
degeneration of images captured in hazy conditions. Remarkable progresses have been …
Global and local similarity learning in multi-kernel space for nonnegative matrix factorization
Most of existing nonnegative matrix factorization (NMF) methods do not fully exploit global
and local similarity information from data. In this paper, we propose a novel local similarity …
and local similarity information from data. In this paper, we propose a novel local similarity …
Exploring discriminative representations for image emotion recognition with CNNs
W Zhang, X He, W Lu - IEEE Transactions on Multimedia, 2019 - ieeexplore.ieee.org
Image emotion recognition aims to automatically categorize the emotion conveyed by an
image. The potential of deep representation has been demonstrated in recent research on …
image. The potential of deep representation has been demonstrated in recent research on …
Graph regularized Lp smooth non-negative matrix factorization for data representation
This paper proposes a Graph regularized Lp smooth non-negative matrix factorization
(GSNMF) method by incorporating graph regularization and Lp smoothing constraint, which …
(GSNMF) method by incorporating graph regularization and Lp smoothing constraint, which …
Meta-learning based prototype-relation network for few-shot classification
X Liu, F Zhou, J Liu, L Jiang - Neurocomputing, 2020 - Elsevier
Pattern recognition has made great progress under large amount of labeled data, while
performs poorly on a very few examples obtained, named few-shot classification, where a …
performs poorly on a very few examples obtained, named few-shot classification, where a …
A novel deep-learning-based QoS prediction model for service recommendation utilizing multi-stage multi-scale feature fusion with individual evaluations
With the rapid development of service computing, the demand for service recommendation
is increasing. Quality of Service (QoS) prediction has been one of the key challenges for …
is increasing. Quality of Service (QoS) prediction has been one of the key challenges for …