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Convolutional Neural Networks [PDF]
, 2022 AbstractWe provide the fundamentals of convolutional neural networks (CNNs) and include several examples using the Keras library. We give a formal motivation for using CNN that clearly shows the advantages of this topology compared to feedforward networks for processing images. Several practical examples with plant breeding data are provided using CNNs
Osval Antonio Montesinos López +2 more
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Convolutional Neural Networks With Dynamic Regularization [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Regularization is commonly used for alleviating overfitting in machine learning. For convolutional neural networks (CNNs), regularization methods, such as DropBlock and Shake-Shake, have illustrated the improvement in the generalization performance. However, these methods lack a self-adaptive ability throughout training.
Yi Wang +3 more
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Convolution-Bidirectional Temporal Convolutional Network for Protein Secondary Structure Prediction
IEEE Access, 2022 As a basic feature extraction method, convolutional neural networks have some information loss problems when dealing with sequence problems, and a temporal convolutional network can compensate for this problem.
Yunqing Zhang, Yuming Ma, Yihui Liu
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Improved Convolutional Neural Image Recognition Algorithm based on LeNet-5
Journal of Computer Networks and Communications, 2022 Convolutional neural network (CNN) is a very important method in deep learning, which solves many complex pattern recognition problems. Fruitful results have been achieved in image recognition, speech recognition, and natural language processing ...
Lijie Zhou, Weihai Yu
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DISTRIBUTED CONVOLUTIONAL NEURAL NETWORK MODEL ON RESOURCE-CONSTRAINED CLUSTER [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2020 Subject of Research. The paper presents the distributed deep learning particularly convolutional neural network problem for resource-constrained devices.
Rezeda R. Khaydarova +3 more
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Pansharpening by Convolutional Neural Networks [PDF]
Remote Sensing, 2016 A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices ...
MASI, GIUSEPPE +3 more
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Method for predicting cutter remaining life based on multi-scale cyclic convolutional network
International Journal of Distributed Sensor Networks, 2022 In the process of predicting the remaining cutter life, the deep-learning method such as convolutional neural network does not consider the time correlation of different degradation states, which directly affects the accuracy of the remaining cutter life
Tao Li +5 more
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Texture synthesis of ecological plant protection image based on convolution neural network
Frontiers in Plant Science, 2022 Texture synthesis technology is an important realistic rendering technology. Texture synthesis technology also has a good application prospect in image rendering and other fields. Convolutional neural network is a very popular technology in recent years.
Libing Hu, Fei Zhou, Xianjun Fu
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Quantum convolutional neural networks [PDF]
Nature Physics, 2019 12 pages, 11 figures. v2: New application to optimizing quantum error correction codes, added sample complexity analysis, more details for experimental realizations, and other minor ...
Iris Cong +2 more
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Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm
IEEE Access, 2020 As an algorithm with excellent performance, convolutional neural network has been widely used in the field of image processing and achieved good results by relying on its own local receptive fields, weight sharing, pooling, and sparse connections.
Youhui Tian
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