Vol. 3 No. 1 (2023): Advances in Deep Learning Techniques
Articles

Capsule Networks - Advancements and Implementations: Investigating Advancements in Capsule Networks and Their Implementations for Improving Robustness in Image Recognition Tasks

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  • Dr. Priya Sharma
Dr. Priya Sharma
Postdoctoral Researcher in Neural Networks, University of Toronto, Canada
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Published 27-02-2024

Keywords

  • Capsule Networks,
  • Deep Learning,
  • Image Recognition,
  • Convolutional Neural Networks,
  • Robustness,
  • Hierarchical Representations,
  • Advancements,
  • Implementations
    • ...More
    Less

How to Cite

[1]
D. P. Sharma, “Capsule Networks - Advancements and Implementations: Investigating Advancements in Capsule Networks and Their Implementations for Improving Robustness in Image Recognition Tasks”, Adv. in Deep Learning Techniques, vol. 3, no. 1, pp. 1–15, Feb. 2024.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

Capsule networks, a novel deep learning architecture proposed by Geoffrey Hinton and colleagues, have garnered significant attention for their potential to improve the robustness of deep learning models in image recognition tasks. Unlike traditional convolutional neural networks (CNNs), capsule networks use capsules as the primary building blocks, allowing for better representation of hierarchical relationships in the data. This paper provides a comprehensive overview of capsule networks, including their architecture, working principles, and advancements. It also explores various implementations of capsule networks in image recognition tasks, highlighting their advantages and limitations. Through a detailed analysis of recent research, this paper aims to shed light on the current state of capsule networks and their potential impact on the field of deep learning.

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