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MallaNet residual branch merge convolutional neural network with homogeneous filter capsules for Devanagari character recognition. [PDF]
Sci RepMalla SR.
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Image-Based Telecom Fraud Detection Method Using an Attention Convolutional Neural Network. [PDF]
Entropy (Basel)Li J, Dang J, Wang Y, Yang J.
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Privacy protection method for ADS-B air traffic control data based on convolutional neural network and symmetric encryption. [PDF]
Front Big DataMa C, Jia R, Lou J, Wang M.
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Extending convolutional neural networks to irregular domains through graph inference
, 2017 Bastien Pasdeloup
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2023
Dieses Kapitel führt in Convolutional Neural Networks (CNNs) ein und beschreibt, wie diese im Kontext der Sportanalyse verwendet werden können. Insbesondere eignen sich CNNs für das End-to-End-Lernen auf Bildern oder ähnlich strukturierten Daten. Dabei können CNNs Merkmale von Bildern anhand der Pixelwerte effizient lernen und beispielsweise sehr gute ...
Teik Toe Teoh, Yu Jin Goh
+6 more sources
Dieses Kapitel führt in Convolutional Neural Networks (CNNs) ein und beschreibt, wie diese im Kontext der Sportanalyse verwendet werden können. Insbesondere eignen sich CNNs für das End-to-End-Lernen auf Bildern oder ähnlich strukturierten Daten. Dabei können CNNs Merkmale von Bildern anhand der Pixelwerte effizient lernen und beispielsweise sehr gute ...
Teik Toe Teoh, Yu Jin Goh
+6 more sources
2021
Convolutional neural network (CNN) is a (Agrawal and Roy, IEEE Trans Magn 55:1–7, 2019) class of deep neural network. CNNs are what we call the most representative supervised model in the theory of deep learning is the technique that nowadays (Akinaga and Shima, Proc IEEE 98:2237–2251, 2010) is producing a lot of outstanding results especially in the ...
Y. V. R. Nagapawan +2 more
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Convolutional neural network (CNN) is a (Agrawal and Roy, IEEE Trans Magn 55:1–7, 2019) class of deep neural network. CNNs are what we call the most representative supervised model in the theory of deep learning is the technique that nowadays (Akinaga and Shima, Proc IEEE 98:2237–2251, 2010) is producing a lot of outstanding results especially in the ...
Y. V. R. Nagapawan +2 more
+5 more sources
Differential convolutional neural network
Neural Networks, 2019Convolutional neural networks with strong representation ability of deep structures have ever increasing popularity in many research areas. The main difference of Convolutional Neural Networks with respect to existing similar artificial neural networks is the inclusion of the convolutional part.
Sarıgül M., Ozyildirim B.M., Avci M.
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