Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods …
Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
MA Abdou - Neural Computing and Applications, 2022 - Springer
Significant evolution in deep learning took place in 2010, when software developers started using graphical processing units for general-purpose applications. From that date, the deep …
J Chen, X Ran - Proceedings of the IEEE, 2019 - ieeexplore.ieee.org
Deep learning is currently widely used in a variety of applications, including computer vision and natural language processing. End devices, such as smartphones and Internet-of-Things …
The objective of this work is speaker recognition under noisy and unconstrained conditions. We make two key contributions. First, we introduce a very large-scale audio-visual dataset …
The rapid growth of urban populations worldwide imposes new challenges on citizens' daily lives, including environmental pollution, public security, road congestion, etc. New …
In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been …
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning …
The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low-cost measure of mesoscale brain dynamics with high temporal resolution …