Convolutional Neural Networks (CNN)
Applications of Convolutional Neural Networks
... deep Convolutional neural network and a Markov random field is successfully applied to the long-standing task of human pose ...Deep Convolutional Neural Networks. These networks ...
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Survey on Convolutional Neural Networks
... Convolutional neural networks (CNNs)are one of the most widely used type of deep artificial neural networks that are used in various fields such as image and video recognition, speech ...
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OVERVIEW OF CONVOLUTIONAL NEURAL NETWORKS
... According to [3], inputs given to convolutional neural networks are an array of pixel values with each value ranging from 0 to 255. For example, if the input is an image of dimensions 50x50 then the ...
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1 Convolutional Neural Networks Architectures
... 1 Convolutional Neural Networks Architectures We will survey the some most famous convolutional neural net ...of convolutional layers, then pooling layers, followed by several ...
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Localization Using Convolutional Neural Networks
... Introduction With the increased accessibility to powerful GPUs, ability to develop machine learning algorithms has increased significantly. Coupled with open source deep learning frameworks, average users are now able to ...
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Convolutional Neural Networks for Sentence Classification
... In the present work we have described a series of experiments with convolutional neural networks built on top of word2vec. Despite little tuning of hyperparameters, a simple CNN with one layer of ...
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Ear detection with convolutional neural networks
... Struc and Peter Peer [6]. The project addressees the identification of people by the biometry of their earlobe. In this thesis the detection of ears in images will be covered. This will help building a database of ...
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Convolutional Neural Networks in Medical Imaging
... 2012 convolutional neural networks or CNNs, were used to great success improving dramatically over the previous state-of-the-arts in the ImageNet computer vision compe- tition ...
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Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
... Convolutional neural networks (CNN) have led to many state-of-the-art results spanning through various ...approximation, Convolutional Sparse Coding (CSC) has gained increasing attention in ...
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A General Purpose Tagger with Convolutional Neural Networks
... {xiangyu,falensaa,thangvu}@ims.uni-stuttgart.de Abstract We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context ...
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Calibration of Convolutional Neural Networks
... LeNet-5 convolutional neural network for digit recogni- tion (LeCun et ...Most convolutional neural networks use also a pooling layer, which makes the representation more invariant to ...
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Notes on Convolutional Neural Networks
... 3 Convolutional Neural Networks Typically convolutional layers are interspersed with sub-sampling layers to reduce computation time and to gradually build up further spatial and configural ...
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Pansharpening by convolutional neural networks
... Abstract: A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the ...
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Labeling Paths with Convolutional Neural Networks
... with Convolutional Neural Networks Kyle Wuerch and Sean Wallace Abstract— With the increasing development of autonomous vehicles, being able to detect driveable paths in arbitrary environments has ...
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Convolutional neural networks for malware classification
... Artificial Neural Networks Artificial Neural Networks are a family of models inspired by the way biolog- ical nervous systems, such as the brain, process information which enables a computer ...
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Robust tracking with convolutional neural networks
... with convolutional neural networks Visual object tracking is a process of object(s) localization through the se- quence of ...use convolutional neural networks for tracking. ...
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Deep convolutional neural networks capabilities for
... for the detection of MCs based on the use of deep convolutional neural networks (DCNNs).. We.[r] ...
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Super Sparse Convolutional Neural Networks
... of Convolutional Neural Networks (CNNs) is usually too large to be deployed on the mobile devices and they often suffer from the over-fitting problem caused by the less abundant ...the ...
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Convolutional neural networks for brain tumour segmentation
... Conclusion Convolutional neural networks remain a growing area of research in automated tumour ...of convolutional neural networks so that they are well posi- tioned to deploy ...
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Cyberbullying Intervention Based on Convolutional Neural Networks
... This paper describes the process of building a cyberbullying intervention interface driven by a machine-learning based text-classification service. We make two main contribu- tions. First, we show that cyberbullying can ...
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