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Quantum Convolutional Neural Networks [PDF]
Nature Physics, 2019 We introduce and analyze a novel quantum machine learning model motivated by convolutional neural networks. Our quantum convolutional neural network (QCNN) makes use of only $O(\log(N))$ variational parameters for input sizes of $N$ qubits, allowing for ...
A Feiguin +51 more
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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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Content-aware convolutional neural networks [PDF]
Neural Networks, 2021 Accepted by Neural ...
Yong Guo +5 more
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Self-grouping convolutional neural networks [PDF]
Neural Networks, 2020 Although group convolution operators are increasingly used in deep convolutional neural networks to improve the computational efficiency and to reduce the number of parameters, most existing methods construct their group convolution architectures by a predefined partitioning of the filters of each convolutional layer into multiple regular filter groups
Qingbei Guo +3 more
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A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much
Zewen Li +4 more
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A noise robust convolutional neural network for image classification
Results in Engineering, 2021 Convolutional Neural Networks (CNNs) are extensively used for image classification. Noisy images reduce the classification performance of convolutional neural networks and increase the training time of the networks.
Mohammad Momeny +4 more
doaj +1 more source
Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way
Sensors, 2023 Optical neural networks can effectively address hardware constraints and parallel computing efficiency issues inherent in electronic neural networks. However, the inability to implement convolutional neural networks at the all-optical level remains a ...
Yaze Yu +4 more
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ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks [PDF]
Computer Vision and Pattern Recognition, 2019 Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs).
Qilong Wang +5 more
semanticscholar +1 more source
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [PDF]
International Conference on 3D Vision, 2016 Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used ...
Fausto Milletarì +2 more
semanticscholar +1 more source
Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network
Inventions, 2021 In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications ...
Elena Solovyeva, Ali Abdullah
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