Now showing items 1-4 of 4
Deep learning for consumer devices and services: Pushing the limits for machine learning, artificial intelligence, and computer vision
(Institute of Electrical and Electronics Engineers (IEEE), 2017-04)
In the last few years, we have witnessed an exponential growth in research activity into the advanced training of convolutional neural networks (CNNs), a field that has become known as deep learning. This has been triggered ...
Smart augmentation learning an optimal data augmentation strategy
(Institute of Electrical and Electronics Engineers (IEEE), 2017-04-24)
A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks. There are many techniques to address this, including ...
Semiparallel deep neural network hybrid architecture: first application on depth from monocular camera
(Society of Photo-optical Instrumentation Engineers (SPIE), 2018-08-07)
Deep neural networks have been applied to a wide range of problems in recent years. Convolutional neural network is applied to the problem of determining the depth from a single camera image (monocular depth). Eight different ...
Pushing the AI envelope: merging deep networks to accelerate edge artificial intelligence in consumer electronics devices and systems
Deep neural networks (DNNs) are widely used by both academic and industry researchers to solve many long-standing problems in machine learning. There has been such a growth of research in this field, and it has been applied ...