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Journal of King Saud University: Computer and Information Sciences, 2022 Modern day computer vision tasks requires efficient solution to problems such as image recognition, natural language processing, object detection, object segmentation and language translation.
Mensah Kwabena Patrick +3 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 ...
David Peer +2 more
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Image Super-Resolution Using Capsule Neural Networks
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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Classification of Defective Fabrics Using Capsule Networks
Applied Sciences, 2022 Fabric quality has an important role in the textile sector. Fabric defect, which is a highly important factor that influences the fabric quality, has become a concept that researchers are trying to minimize. Due to the limited capacity of human resources,
Yavuz Kahraman, Alptekin Durmuşoğlu
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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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Capsule Networks with Routing Annealing
Lecture Notes in Computer Science, 2021 Capsule Networks overcome some shortcomings of convolutional neural networks organizing neurons into groups of capsules. Capsule layers are dynamically connected by means of an iterative routing mechanism, which models the connection strengths between capsules from different layers.
Marco Grangetto +2 more
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A text classification method by integrating mobile inverted residual bottleneck convolution networks and capsule networks with adaptive feature channels [PDF]
Scientific ReportsThis study proposes a novel text classification model, MBConv-CapsNet, to address large-scale text data classification issues in the Internet era. Integrating the advantages of Mobile Inverted Bottleneck Convolutional Networks and Capsule Networks, this ...
Tao Jin, Jiaming Liu
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Detection of tuberculosis using cough audio analysis: a deep learning approach with capsule networks
Discover Artificial IntelligencePurpose Tuberculosis (TB) is a widespread infectious disease that requires early detection for effective treatment and control. This study aims to improve TB detection using cough audio analysis, comparing the performance of capsule networks to other ...
Sakthi Jaya Sundar Rajasekar +6 more
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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 Özcan +2 more
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From Auto-encoders to Capsule Networks: A Survey [PDF]
E3S Web of Conferences, 2021 Convolutional Neural Networks are a very powerful Deep Learning algorithm used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains.
El Alaoui-Elfels Omaima, Gadi Taoufiq
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