From Auto-encoders to Capsule Networks: A Survey

Convolutional Neural Networks are a very powerful Deep Learning structure used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and rel...
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Autor*in:	El Alaoui-Elfels Omaima [verfasserIn] Gadi Taoufiq [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch ; Französisch

Erschienen:	2021

Schlagwörter:	convolutional neural networks auto-encoders capsule networks routing by agreement between capsules em routing stacked capsule network deep learning.

Übergeordnetes Werk:	In: E3S Web of Conferences - EDP Sciences, 2013, 229, p 01003(2021)
Übergeordnetes Werk:	volume:229, p 01003 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1051/e3sconf/202122901003

Katalog-ID:	DOAJ050093185

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source	In E3S Web of Conferences 229, p 01003(2021) volume:229, p 01003 year:2021
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callnumber-first	G - Geography, Anthropology, Recreation
author	El Alaoui-Elfels Omaima
spellingShingle	El Alaoui-Elfels Omaima misc GE1-350 misc convolutional neural networks misc auto-encoders misc capsule networks misc routing by agreement between capsules misc em routing misc stacked capsule network misc deep learning. misc Environmental sciences From Auto-encoders to Capsule Networks: A Survey
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topic_title	GE1-350 From Auto-encoders to Capsule Networks: A Survey convolutional neural networks auto-encoders capsule networks routing by agreement between capsules em routing stacked capsule network deep learning
topic	misc GE1-350 misc convolutional neural networks misc auto-encoders misc capsule networks misc routing by agreement between capsules misc em routing misc stacked capsule network misc deep learning. misc Environmental sciences
topic_unstemmed	misc GE1-350 misc convolutional neural networks misc auto-encoders misc capsule networks misc routing by agreement between capsules misc em routing misc stacked capsule network misc deep learning. misc Environmental sciences
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title	From Auto-encoders to Capsule Networks: A Survey
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title_auth	From Auto-encoders to Capsule Networks: A Survey
abstract	Convolutional Neural Networks are a very powerful Deep Learning structure used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks(CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series architectures to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-theartof Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets.
abstractGer	Convolutional Neural Networks are a very powerful Deep Learning structure used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks(CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series architectures to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-theartof Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets.
abstract_unstemmed	Convolutional Neural Networks are a very powerful Deep Learning structure used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks(CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series architectures to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-theartof Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets.
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title_short	From Auto-encoders to Capsule Networks: A Survey
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