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Image Super-Resolution Using Capsule Neural Networks [PDF]
IEEE Access, 2020 Convolutional neural networks (CNNs) have been widely applied in super-resolution (SR) and other image restoration tasks. Recently, Hinton et al. proposed capsule neural networks to resolve the problem of viewpoint variations in image classification ...
Jui-Ting Hsu, Chih-Hung Kuo, De-Wei Chen
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Facial keypoints detection using capsule neural networks
Научно-технический вестник информационных технологий, механики и оптики, 2023 The problem of detecting key points of the face is investigated. This problem is quite relevant and important. The existing approaches of solving this problem, which are usually divided into parametric and nonparametric methods, are considered.
A. A. Boitsev +4 more
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Heart Murmur Classification Using a Capsule Neural Network
Bioengineering, 2023 The healthcare industry has made significant progress in the diagnosis of heart conditions due to the use of intelligent detection systems such as electrocardiograms, cardiac ultrasounds, and abnormal sound diagnostics that use artificial intelligence ...
Yu-Ting Tsai +4 more
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Polyphonic Sound Event Detection by using Capsule Neural Networks [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2019 Artificial sound event detection (SED) has the aim to mimic the human ability to perceive and understand what is happening in the surroundings. Nowadays, Deep Learning offers valuable techniques for this goal such as Convolutional Neural Networks (CNNs).
Gabrielli, Leonardo +3 more
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Detecting fake news with capsule neural networks [PDF]
Applied Soft Computing, 2021 Fake news is dramatically increased in social media in recent years. This has prompted the need for effective fake news detection algorithms. Capsule neural networks have been successful in computer vision and are receiving attention for use in Natural Language Processing (NLP).
Goldani, Mohammad Hadi +2 more
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Capsule neural network and its applications in drug discovery
iScienceSummary: Deep learning holds great promise in drug discovery, yet its application is hindered by high labeling costs and limited datasets. Developing algorithms that effectively learn from sparsely labeled data is crucial.
Yiwei Wang +7 more
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Improved Two-Branch Capsule Network for Hyperspectral Image Classification [PDF]
Jisuanji kexue yu tansuo, 2022 The method based on the dual-channel capsule network extracts spectral information and spatial informa-tion separately in two channels, which not only retains the feature extraction method of the dual-channel convolu-tional neural network, but also ...
ZHANG Haitao, CHAI Simin
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ECG signal classification using capsule neural networks [PDF]
IET Networks, 2021 Abstract Cardiovascular diseases (CVD) are the dominant cause of deaths in the world, of which 90% are curable. The electrocardiogram (ECG) measures the electrical stimulus of the heart noninvasively. Convolutional neural networks (CNN) act as one of the powerful machine learning techniques to classify ECG arrhythmia classification ...
Tejashwini Neela, Swetha Namburu
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Multi-branch RA Capsule Network and Its Application in Image Classification [PDF]
Jisuanji kexue, 2022 Capsule Network is a new type of deep neural network that uses vectors to express information of image feature and overcomes two major problems of convolutional neural networks by introducing dynamic routing algorithms.First,convolutional neural networks
WU Lin, SUN Jing-yu
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Residual Vector Capsule: Improving Capsule by Pose Attention
IEEE Access, 2021 The convolutional neural network has significantly improved the accuracy of image recognition; however, it performs in a fragile manner when we apply viewpoint transformation or add noise to the image.
Ning Xie, Xiaoxia Wan
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