DL-CapsNet: A Deep and Light Capsule Network

Fuente: arXiv
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Auteurs principaux: Shiri, Pouya, Baniasadi, Amirali
Format: Preprint
Publié: 2025
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author Shiri, Pouya
Baniasadi, Amirali
author_facet Shiri, Pouya
Baniasadi, Amirali
contents Capsule Network (CapsNet) is among the promising classifiers and a possible successor of the classifiers built based on Convolutional Neural Network (CNN). CapsNet is more accurate than CNNs in detecting images with overlapping categories and those with applied affine transformations. In this work, we propose a deep variant of CapsNet consisting of several capsule layers. In addition, we design the Capsule Summarization layer to reduce the complexity by reducing the number of parameters. DL-CapsNet, while being highly accurate, employs a small number of parameters and delivers faster training and inference. DL-CapsNet can process complex datasets with a high number of categories.
format Preprint
id arxiv_https___arxiv_org_abs_2512_00061
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle DL-CapsNet: A Deep and Light Capsule Network
Shiri, Pouya
Baniasadi, Amirali
Computer Vision and Pattern Recognition
Capsule Network (CapsNet) is among the promising classifiers and a possible successor of the classifiers built based on Convolutional Neural Network (CNN). CapsNet is more accurate than CNNs in detecting images with overlapping categories and those with applied affine transformations. In this work, we propose a deep variant of CapsNet consisting of several capsule layers. In addition, we design the Capsule Summarization layer to reduce the complexity by reducing the number of parameters. DL-CapsNet, while being highly accurate, employs a small number of parameters and delivers faster training and inference. DL-CapsNet can process complex datasets with a high number of categories.
title DL-CapsNet: A Deep and Light Capsule Network
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2512.00061