Topics
convolutionMilitary conflict
Convolutional Neural NetworkBroadcaster
When a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method of Deep Shifting, which remembers previously calculated results of convolution operations in order to minimize the number of calculations. The reduction in complexity is at least a constant and in the best case quadratic. We demonstrate that this method does indeed save significant computation time in a practical implementation, especially when the networks receives a large number of time-frames
Part 2: Deep LearningInternational audienceA conventional convolutional neural network (CNN) is trai...
Deep neural networks are quickly advancing and increasingly used in many applications; however, thes...
Convolutional Neural Networks (CNNs) are extensively used in a wide range of applications, commonly ...
textabstractWhen a Convolutional Neural Network is used for on-the-fly evaluation of continuously u...
Human learners can readily understand speech, or a melody, when it is presented slower or faster tha...
Deep convolutional neural networks (ConvNets), which are at the heart of many new emerging applicati...
Deep convolutional neural networks (ConvNets), which are at the heart of many new emerging applicati...
A neural network that matches with a complex data function is likely to boost the classification per...
Deep convolutional neural networks (CNNs), which are at the heart of many new emerging applications,...
We present an automated computer vision architecture to handle video and image data using the same b...
With the introduction of deep learning, machine learning has dominated several technology areas, giv...
Deep Convolutional Neural Networks have found wide application but their training time can be signi...
International audienceTransfer learning for deep neural networks is the process of first training a ...
Temporal Convolutional Networks (TCNs) are promising Deep Learning models for time-series processing...
Video understanding is one of the fundamental problems in computer vision. Videos provide more infor...
Part 2: Deep LearningInternational audienceA conventional convolutional neural network (CNN) is trai...
Deep neural networks are quickly advancing and increasingly used in many applications; however, thes...
Convolutional Neural Networks (CNNs) are extensively used in a wide range of applications, commonly ...
textabstractWhen a Convolutional Neural Network is used for on-the-fly evaluation of continuously u...
Human learners can readily understand speech, or a melody, when it is presented slower or faster tha...
Deep convolutional neural networks (ConvNets), which are at the heart of many new emerging applicati...
Deep convolutional neural networks (ConvNets), which are at the heart of many new emerging applicati...
A neural network that matches with a complex data function is likely to boost the classification per...
Deep convolutional neural networks (CNNs), which are at the heart of many new emerging applications,...
We present an automated computer vision architecture to handle video and image data using the same b...
With the introduction of deep learning, machine learning has dominated several technology areas, giv...
Deep Convolutional Neural Networks have found wide application but their training time can be signi...
International audienceTransfer learning for deep neural networks is the process of first training a ...
Temporal Convolutional Networks (TCNs) are promising Deep Learning models for time-series processing...
Video understanding is one of the fundamental problems in computer vision. Videos provide more infor...
Part 2: Deep LearningInternational audienceA conventional convolutional neural network (CNN) is trai...
Deep neural networks are quickly advancing and increasingly used in many applications; however, thes...
Convolutional Neural Networks (CNNs) are extensively used in a wide range of applications, commonly ...
Add to ORKG