Results 221 to 230 of about 449,358 (260)
Web-Based Sustainable Detection and Treatment Recommendation System for Wheat Plant Diseases Using Convolutional Neural Networks. [PDF]
Food Sci NutrAbbasi NG +7 more
europepmc +1 more source
DMSCA: dynamic multi-scale channel-spatial attention for enhanced feature representation in convolutional neural networks. [PDF]
Sci RepZong L, Nan SJ, Die ZF, Peng HJ.
europepmc +1 more source
Correction for Srivastava et al., Emergent neuronal mechanisms mediating covert attention in convolutional neural networks. [PDF]
Proc Natl Acad Sci U S Aeuropepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
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
+5 more sources
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.
openaire +3 more sources
2021
In this chapter we introduce the convolutional neural network theory including concepts such as convolution operator, kernel, stride, padding and pooling.
Ullo S. L. +7 more
openaire +2 more sources
In this chapter we introduce the convolutional neural network theory including concepts such as convolution operator, kernel, stride, padding and pooling.
Ullo S. L. +7 more
openaire +2 more sources