Search Results for author:
Found 14 papers, 3 papers with code
Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and Correntropy
We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
Feedback Pyramid Attention Networks for Single Image Super-Resolution
Recently, convolutional neural network (CNN) based image super-resolution (SR) methods have achieved significant performance improvement.
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution
Recently, deep convolutional neural network methods have achieved an excellent performance in image superresolution (SR), but they can not be easily applied to embedded devices due to large memory cost.
A Comprehensive Survey and Taxonomy on Image Dehazing Based on Deep Learning
With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed.
Learning Rates for Multi-task Regularization Networks
Multi-task learning is an important trend of machine learning in facing the era of artificial intelligence and big data.
Delving into Variance Transmission and Normalization: Shift of Average Gradient Makes the Network Collapse
We propose Parametric Weights Standardization (PWS), a fast and robust to mini-batch size module used for conv filters, to solve the shift of the average gradient.
Randomized Kernel Multi-view Discriminant Analysis
In this paper, we propose the kernel version of multi-view discriminant analysis, called kernel multi-view discriminant analysis (KMvDA).
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Generative adversarial networks (GANs) are a hot research topic recently.
Deep Human Answer Understanding for Natural Reverse QA
This study focuses on a reverse question answering (QA) procedure, in which machines proactively raise questions and humans supply the answers.
Multi-grained Attention Networks for Single Image Super-Resolution
In this paper, we make a thorough investigation on the attention mechanisms in a SR model and shed light on how simple and effective improvements on these ideas improve the state-of-the-arts.
AHash: A Load-Balanced One Permutation Hash
Unlike the densification to fill the empty bins after they undesirably occur, our design goal is to balance the load so as to reduce the empty bins in advance.
Large Margin Multi-modal Multi-task Feature Extraction for Image Classification
The features used in many image analysis-based applications are frequently of very high dimension.
Fast Supervised Discrete Hashing
Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm.
