Results 281 to 290 of about 229,861 (318)
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International Journal of Bifurcation and Chaos, 2003
A systematic design methodology for finding CNN parameters with prescribed functions is proposed. A given function (task) is translated into several local operations, and they are realized as stable states of the CNN system. Many CNN parameters (CNN genes) with the same functions can be easily derived by using this design methodology.
Makoto Itoh, Leon O. Chua
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A systematic design methodology for finding CNN parameters with prescribed functions is proposed. A given function (task) is translated into several local operations, and they are realized as stable states of the CNN system. Many CNN parameters (CNN genes) with the same functions can be easily derived by using this design methodology.
Makoto Itoh, Leon O. Chua
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International Journal of Bifurcation and Chaos, 1997
CNN is an acronym for either Cellular Neural Network when used in the context of brain science, or Cellular Nonlinear Network when used in the context of coupled dynamical systems. A CNN is defined by two mathematical constructs: 1. A spatially discrete collection of continuous nonlinear dynamical systems called cells, where information can be ...
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CNN is an acronym for either Cellular Neural Network when used in the context of brain science, or Cellular Nonlinear Network when used in the context of coupled dynamical systems. A CNN is defined by two mathematical constructs: 1. A spatially discrete collection of continuous nonlinear dynamical systems called cells, where information can be ...
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2018
The main aim of this work is to present a Quaternion Phase Convolutional Neural Network. We encode 3 quaternion phases and its magnitude as an input. Our approach is bio-inspired and is expressed in one mathematical framework. The main result is to obtain a new space feature representation for deep learning which can capture non-trivial equivariant ...
Eduardo Ulises Moya-Sánchez +3 more
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The main aim of this work is to present a Quaternion Phase Convolutional Neural Network. We encode 3 quaternion phases and its magnitude as an input. Our approach is bio-inspired and is expressed in one mathematical framework. The main result is to obtain a new space feature representation for deep learning which can capture non-trivial equivariant ...
Eduardo Ulises Moya-Sánchez +3 more
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2016
Global convolutional neural networks (CNNs) activations lack geometric invariance, and in order to address this problem, Gong et al. proposed multi-scale orderless pooling(MOP-CNN), which extracts CNN activations for local patches at multiple scale levels, and performs orderless VLAD pooling to extract features.
Dan Yu, Xiao-Jun Wu 0001
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Global convolutional neural networks (CNNs) activations lack geometric invariance, and in order to address this problem, Gong et al. proposed multi-scale orderless pooling(MOP-CNN), which extracts CNN activations for local patches at multiple scale levels, and performs orderless VLAD pooling to extract features.
Dan Yu, Xiao-Jun Wu 0001
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Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN
Sensors, 2022Xiangyang Xu, Peixin Shi, Ruiqi Ren
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CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope
Electronics (Switzerland), 2021Dulari Bhatt +2 more
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CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis
Advanced Engineering Informatics, 2023Jianping Yan, Clemens Gühmann
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Assamese document classification using CNN, multi-channel CNN and CNN-SVM
AIP Conference Proceedings, 2023Chayanika Talukdar, Shikhar Kumar Sarma
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HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification
IEEE Geoscience and Remote Sensing Letters, 2020Swalpa Kumar Roy +2 more
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Automatic depression recognition using CNN with attention mechanism from videos
Neurocomputing, 2021Lang He +2 more
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