research
∙
08/31/2023
Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images
Generating 3D faces from textual descriptions has a multitude of applica...
share
research
∙
05/13/2023
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning
Meta learning recently has been heavily researched and helped advance th...
research
∙
03/23/2023
Keypoint-Guided Optimal Transport
Existing Optimal Transport (OT) methods mainly derive the optimal transp...
research
∙
01/18/2023
Improve Noise Tolerance of Robust Loss via Noise-Awareness
Robust loss minimization is an important strategy for handling robust le...
research
∙
09/21/2022
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
Although current deep learning-based methods have gained promising perfo...
research
∙
04/01/2022
LDP-IDS: Local Differential Privacy for Infinite Data Streams
Streaming data collection is essential to real-time data analytics in va...
research
∙
02/11/2022
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
Modern deep neural networks can easily overfit to biased training data c...
research
∙
07/30/2021
Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions
It has been shown that equivariant convolution is very helpful for many ...
research
∙
07/26/2021
A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation
Cross-contrast image translation is an important task for completing mis...
research
∙
07/06/2021
Learning an Explicit Hyperparameter Prediction Policy Conditioned on Tasks
Meta learning has attracted much attention recently in machine learning ...
research
∙
03/12/2021
Training Networks in Null Space of Feature Covariance for Continual Learning
In the setting of continual learning, a network is trained on a sequence...
research
∙
02/06/2021
Learning adaptive differential evolution algorithm from optimization experiences by policy gradient
Differential evolution is one of the most prestigious population-based s...
research
∙
01/21/2021
Robust spectral compressive sensing via vanilla gradient descent
This paper investigates robust recovery of an undamped or damped spectra...
research
∙
11/03/2020
Amortized Variational Deep Q Network
Efficient exploration is one of the most important issues in deep reinfo...
research
∙
09/02/2020
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction
Current machine learning has made great progress on computer vision and ...
research
∙
07/29/2020
Meta-LR-Schedule-Net: Learned LR Schedules that Scale and Generalize
The learning rate (LR) is one of the most important hyper-parameters in ...
research
∙
07/10/2020
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
The recent advancement of deep learning techniques has made great progre...
research
∙
06/06/2020
Graph Neural Network Encoding for Community Detection in Attribute Networks
In this paper, we first propose a graph neural network encoding method f...
research
∙
03/10/2020
Learning to be Global Optimizer
The advancement of artificial intelligence has cast a new light on the d...
research
∙
03/04/2020
On Hyper-parameter Tuning for Stochastic Optimization Algorithms
This paper proposes the first-ever algorithmic framework for tuning hype...
research
∙
03/02/2020
Adaptive Structural Hyper-Parameter Configuration by Q-Learning
Tuning hyper-parameters for evolutionary algorithms is an important issu...
research
∙
02/16/2020
Learning Adaptive Loss for Robust Learning with Noisy Labels
Robust loss minimization is an important strategy for handling robust le...
research
∙
07/08/2019
Variational Bayes' method for functions with applications to some inverse problems
Bayesian approach as a useful tool for quantifying uncertainties has bee...
research
∙
02/20/2019
Push the Student to Learn Right: Progressive Gradient Correcting by Meta-learner on Corrupted Labels
While deep networks have strong fitting capability to complex input patt...
research
∙
02/20/2019
Tug the Student to Learn Right: Progressive Gradient Correcting by Meta-learner on Corrupted Labels
While deep networks have strong fitting capability to complex input patt...
research
∙
01/10/2019
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Hyperspectral imaging can help better understand the characteristics of ...
research
∙
11/22/2018
HyperAdam: A Learnable Task-Adaptive Adam for Network Training
Deep neural networks are traditionally trained using human-designed stoc...
research
∙
09/17/2018
Model-Driven Deep Learning for Physical Layer Communications
Intelligent communication is gradually considered as the mainstream dire...
research
∙
09/12/2018
Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN
The cycleGAN is becoming an influential method in medical image synthesi...
research
∙
08/14/2018
Small Sample Learning in Big Data Era
As a promising area in artificial intelligence, a new learning paradigm,...
research
∙
07/29/2018
Semi-supervised CNN for Single Image Rain Removal
Single image rain removal is a typical inverse problem in computer visio...
research
∙
04/30/2018
Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy
A large number of recent genome-wide association studies (GWASs) for com...
research
∙
03/22/2018
Learning through deterministic assignment of hidden parameters
Supervised learning frequently boils down to determining hidden and brig...
research
∙
09/27/2017
Neural Multi-Atlas Label Fusion: Application to Cardiac MR Images
Multi-atlas segmentation approach is one of the most widely-used image s...
research
∙
06/20/2017
SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning
It is known that Boosting can be interpreted as a gradient descent techn...
research
∙
05/19/2017
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI
Compressive sensing (CS) is an effective approach for fast Magnetic Reso...
research
∙
02/01/2017
Denoising Hyperspectral Image with Non-i.i.d. Noise Structure
Hyperspectral image (HSI) denoising has been attracting much research at...
research
∙
01/06/2016
Low-rank Matrix Factorization under General Mixture Noise Distributions
Many computer vision problems can be posed as learning a low-dimensional...
research
∙
11/10/2015
Deep Representation of Facial Geometric and Photometric Attributes for Automatic 3D Facial Expression Recognition
In this paper, we present a novel approach to automatic 3D Facial Expres...
research
∙
03/06/2015
Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements
Background subtraction has been a fundamental and widely studied task in...
research
∙
03/02/2015
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
In this paper, we address the problem of estimating and removing non-uni...
research
∙
02/10/2015
Video Primal Sketch: A Unified Middle-Level Representation for Video
This paper presents a middle-level video representation named Video Prim...
research
∙
05/23/2014
On the Optimal Solution of Weighted Nuclear Norm Minimization
In recent years, the nuclear norm minimization (NNM) problem has been at...
research
∙
03/31/2014
Sparse K-Means with ℓ_∞/ℓ_0 Penalty for High-Dimensional Data Clustering
Sparse clustering, which aims to find a proper partition of an extremely...
research
∙
12/19/2013
Learning rates of l^q coefficient regularization learning with Gaussian kernel
Regularization is a well recognized powerful strategy to improve the per...
research
∙
10/27/2013
Compressed Sensing SAR Imaging with Multilook Processing
Multilook processing is a widely used speckle reduction approach in synt...
research
∙
11/30/2012
