Title:	ANALYSIS OF CAPSULE NETWORKS FOR IMAGE CLASSIFICATION
Author(s):	Evgin Goceri
ISBN:	978-989-8704-32-0
Editors:	Yingcai Xiao, Ajith Abraham and Guo Chao Peng
Year:	2021
Edition:	Single
Keywords:	Image Classification, Capsule Network, Dynamic Routing, Deep Network, Convolutional Neural Networks
Type:	Full
First Page:	53
Last Page:	60
Language:	English
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Paper Abstract:	Recently, the interest in convolutional neural networks have grown exponentially for image classification. Their success is based on the ability to learn hierarchical and meaningful image representation that results in a feature extraction technique which is general, flexible and can encode complex patterns. However, these networks have some drawbacks. For example, they need a large number of labeled data, lose valuable information (about the internal properties, such as shape, location, pose and orientation in an image and their relationships) in the pooling and are not able to encode deformation information. Therefore, capsule based networks have been introduced as an alternative to convolutional neural networks. Capsules are a group of neurons (logistic units) representing the presence of an entity and the vector indicating the relationship between features by encoding instantiation parameters. Unlike convolutional neural networks, maximum pooling layers are not employed in a capsule network, but a dynamic routing mechanism is applied iteratively in order to decide the coupling of capsules between successive layers. In other words, training between capsule layers is provided with a routing-by-agreement method. However, capsule networks and their properties to provide high accuracy for image classification have not been sufficiently investigated. Therefore, this paper aims (i) to point out drawbacks of convolutional networks, (ii) to examine capsule networks, (iii) to present advantages, weaknesses and strengths of capsule networks proposed for image classification.
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