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Gesture recognition based on convolutional neural network
Cheng Peng
This topic uses convolutional neural network deep learning algorithm technology to design a gesture
Video Sequence Filtering Based On Convolutional Neural Network
Nayyef Mohanad Nidhal Nayyef
Video Sequence Filtering Based On Convolutional Neural Network
Lightweight Deep Neural Networks for Dense Crowd Counting Estimation
Sholtanyuk, S., Leunikau, A.
In this paper, productiveness problems of deep neural networks for dense crowd counting prediction
Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification
Khudhur A.M., Alwan M.H., Al Omairi Q., Mahmood O.A., Hammadi Y.I., Muthanna M.S.A., Aziz A., Muthanna A.
neural network, which exhibits both robustness and efficiency in its classification performance. Thus
Neural network technology to search for targets in remote sensing images of the Earth
Abramov N.S., Talalayev А.А., Fralenko V.P., Shishkin O.G., Khachumov V.M.
images: a convolutional neural network from the nnForge library and a network of the Darknet type. A
Urban areas analysis using satellite image segmentation and deep neural network
Khryaschev V., Ivanovsky L.
The goal of our research was to develop methods based on convolutional neural networks
Comparison of Different Convolutional Neural Network Architectures for Satellite Image Segmentation
Khryashchev V., Ivanovsky L., Pavlov V., Ostrovskaya A., Rubtsov A.
Convolutional neural networks for detection geo-objects on the satellite images from DSTL, Landsat
Convolution Neural Network Learning Features for Handwritten Digit Recognition
Kayumov Z., Tumakov D.
© 2020 IEEE. The classical six-layer neural network is considered. This network is used
Deep Neural Networks for Emotion Recognition
Shchetinin E.Y., Sevastianov L.A., Kulyabov D.S., Ayrjan E.A., Demidova A.V.
methods. Deep convolutional neural networks and recurrent neural networks with bidirectional LSTM memory
Compressing a convolution neural network based on quantization
Pertsau, D., Lukashevich, M., Kupryianava, D.
. In this paper we experimentally investigate approaches to compression of convolutional neural network

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