Results 41 to 50 of about 54,299 (266)
IEEE Access, 2023 Aiming at the problem of insufficient feature extraction and low recognition rate of traditional convolutional neural network in facial expression recognition, a multi-layer feature recognition algorithm based on three-channel convolutional neural ...
Ying He +3 more
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Convolutional Neural Networks In Convolution
CoRR, 2018 Currently, increasingly deeper neural networks have been applied to improve their accuracy. In contrast, We propose a novel wider Convolutional Neural Networks (CNN) architecture, motivated by the Multi-column Deep Neural Networks and the Network In Network(NIN), aiming for higher accuracy without input data transmutation.
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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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Real-Time Image Super-Resolution Using Recursive Depthwise Separable Convolution Network
IEEE Access, 2019 In recent years, deep convolutional neural networks (CNNs) have been widely used for image super-resolution (SR) to achieve a range of sophisticated performances.
Kwok-Wai Hung +2 more
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Generalized Quantum Convolution for Multidimensional Data
Entropy, 2023 The convolution operation plays a vital role in a wide range of critical algorithms across various domains, such as digital image processing, convolutional neural networks, and quantum machine learning.
Mingyoung Jeng +8 more
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An Introduction to Convolutional Neural Networks
CoRR, 2015 10 pages, 5 ...
Keiron O'Shea, Ryan Nash
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Doubly Convolutional Neural Networks
CoRR, 2016 Building large models with parameter sharing accounts for most of the success of deep convolutional neural networks (CNNs). In this paper, we propose doubly convolutional neural networks (DCNNs), which significantly improve the performance of CNNs by further exploring this idea.
Shuangfei Zhai +3 more
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Factorial Convolution Neural Networks
CoRR, 2021 In recent years, GoogleNet has garnered substantial attention as one of the base convolutional neural networks (CNNs) to extract visual features for object detection. However, it experiences challenges of contaminated deep features when concatenating elements with different properties.
Jaemo Sung, Eun-Sung Jung
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Rotation Invariant Local Binary Convolution Neural Networks
IEEE Access, 2018 Convolutional neural networks (CNNs) have achieved unprecedented successes in computer vision fields, but they remain challenged by the problem about how to effectively process the orientation transformation of objects with fewer parameters.
Xin Zhang +5 more
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Compressing Convolutional Neural Networks
CoRR, 2015 Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers.
Wenlin Chen +4 more
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