Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

The Generative Models have gained considerable attention in unsupervised learning via a
new and practical framework called Generative Adversarial Networks (GAN) due to their …

Deep learning has been successfully applied to solve various complex problems ranging
from big data analytics to computer vision and human-level control. Deep learning advances …

As one of the most popular topics in polarimetric synthetic aperture radar (PolSAR)
community, PolSAR image classification has always been an important way for PolSAR …

The need for medical image encryption is increasingly pronounced, for example, to
safeguard the privacy of the patients' medical imaging data. In this article, a novel deep …

Extracting effective features is always a challenging problem for texture classification
because of the uncertainty of scales and the clutter of textural patterns. For texture …

Deep learning is an effective and useful technique that has been widely applied in a variety
of fields, including computer vision, machine vision, and natural language processing …

Image denoising is a challenging inverse problem due to complex scenes and information
loss. Recently, various methods have been considered to solve this problem by building a …

Deep learning methods have been widely studied for Polarimetric synthetic aperture radar
(PolSAR) land cover classification. The scarcity of PolSAR labeled samples and the small …

Synthetic aperture radar (SAR) image classification using deep neural network has drawn
great attention, which generally requires various layers of deep model for feature learning …