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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
Deep learning approaches to biomedical image segmentation
Kurbanov, S. K., Beknazarova, S. S.
of these achievements is the significant ability of deep learning approaches to obtain hierarchical representations
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
Recognition of buildings on satellite images
Qing Bu, Miroevskiy, A.
on practical: development of methods for solving the problem of determining structures on images using neural
Convolutional Neural Networks for Image Steganalysis
Bashkirova D.
computer vision problems. One of these models—Convolutional Neural Network—has been proven to be very
Voice Detection Using Convolutional Neural Network
Vishniakou, U. A., Shaya, B. H.
technologies for convolutional neural network and its use for voice (cough) recognition. Tasks of article
Edge-guided and hierarchical aggregation network for robust medical image segmentation
Yi Tang, Di Zhao, Pertsau, D., Gourinovitch, A., Kupryianav, D.
Medical image segmentation with the convolutional neural networks (CNNs), significantly enhances
Application of Convolutional Neural Networks for Image Detection and Recognition Based on a Self-written Generator
Bienvenue M.M.N., Kozyrev D.
objects using convolutional neural networks is considered. The purpose of the work is to implement
Convolutional Neural Networks for Image Steganalysis
Bashkirova D.
computer vision problems. One of these models—Convolutional Neural Network—has been proven to be very

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