Results 11 to 20 of about 156,643 (272)
3D Point Capsule Networks [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018 In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
Birdal, Tolga +3 more
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Deep Tensor Capsule Network [PDF]
IEEE Access, 2020 Capsule network is a promising model in computer vision. It has achieved excellent results on simple datasets such as MNIST, but the performance deteriorates as data becomes complicated.
Kun Sun +3 more
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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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Quaternion Capsule Networks [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 Capsules are grouping of neurons that allow to represent sophisticated information of a visual entity such as pose and features. In the view of this property, Capsule Networks outperform CNNs in challenging tasks like object recognition in unseen viewpoints, and this is achieved by learning the transformations between the object and its parts with the ...
Baris Ozcan, Furkan Kinli, Furkan Kirac
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Quantum Science and Technology, 2022 Abstract Capsule networks (CapsNets), which incorporate the paradigms of connectionism and symbolism, have brought fresh insights into artificial intelligence (AI). The capsule, as the building block of CapsNets, is a group of neurons represented by a vector to encode different features of an entity.
Zidu Liu +4 more
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Residual Capsule Network [PDF]
2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2019 Convolution Neural Network (CNN) has been the most influential innovations in the filed of Computer Vision. CNN have shown a substantial improvement in the field of Machine Learning. But they do come with their own set of drawbacks - CNN need a large dataset, hyperparameter tuning is nontrivial and importantly, they lose all the internal information ...
Sree Bala Shruthi Bhamidi +1 more
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, 2022 Capsule networks are a class of neural networks that achieved promising results on many computer vision tasks. However, baseline capsule networks have failed to reach state-of-the-art results on more complex datasets due to the high computation and memory requirements.
Gugglberger, Josef +2 more
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Multikernel Capsule Network for Schizophrenia Identification [PDF]
IEEE Transactions on Cybernetics, 2022 Objective: Schizophrenia seriously affects the quality of life. To date, both simple (linear discriminant analysis) and complex (deep neural network) machine learning methods have been utilized to identify schizophrenia based on functional connectivity features.
Tian Wang +3 more
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Bipartite graph capsule network [PDF]
World Wide Web, 2022 AbstractGraphs have been widely adopted in various fields, where many graph models are developed. Most of previous research focuses on unipartite or homogeneous graph analysis. In this graphs, the relationships between the same type of entities are preserved in the graphs.
Xianhang Zhang +5 more
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Limitation of capsule networks [PDF]
Pattern Recognition Letters, 2021 A recently proposed method in deep learning groups multiple neurons to capsules such that each capsule represents an object or part of an object. Routing algorithms route the output of capsules from lower-level layers to upper-level layers. In this paper, we prove that state-of-the-art routing procedures decrease the expressivity of capsule networks ...
Peer, David +2 more
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