We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
Recently, convolutional neural network (CNN) based image super-resolution (SR) methods have achieved significant performance improvement.
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.
With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed.
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.
Generative adversarial networks (GANs) are a hot research topic recently.
This study focuses on a reverse question answering (QA) procedure, in which machines proactively raise questions and humans supply the answers.
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.
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.
The features used in many image analysis-based applications are frequently of very high dimension.
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.