 [2305.11178] Vanishing Activations: A Symptom of Deep Capsule Networks




























  








Skip to main content





Grab your spot at the free arXiv Accessibility Forum
Forum Schedule

We gratefully acknowledge support fromthe Simons Foundation, Stockholm University, and all contributors. Donate





 > cs > arXiv:2305.11178
  





Help | Advanced Search




All fields
Title
Author
Abstract
Comments
Journal reference
ACM classification
MSC classification
Report number
arXiv identifier
DOI
ORCID
arXiv author ID
Help pages
Full text




Search















open search






GO



open navigation menu


quick links

Login
Help Pages
About












Computer Science > Computer Vision and Pattern Recognition


arXiv:2305.11178 (cs)
    




  [Submitted on 13 May 2023]
Title:Vanishing Activations: A Symptom of Deep Capsule Networks
Authors:Miles Everett, Mingjun Zhong, Georgios Leontidis View a PDF of the paper titled Vanishing Activations: A Symptom of Deep Capsule Networks, by Miles Everett and 1 other authors
View PDF

Abstract:Capsule Networks, an extension to Neural Networks utilizing vector or matrix representations instead of scalars, were initially developed to create a dynamic parse tree where visual concepts evolve from parts to complete objects. Early implementations of Capsule Networks achieved and maintain state-of-the-art results on various datasets. However, recent studies have revealed shortcomings in the original Capsule Network architecture, notably its failure to construct a parse tree and its susceptibility to vanishing gradients when deployed in deeper networks. This paper extends the investigation to a range of leading Capsule Network architectures, demonstrating that these issues are not confined to the original design. We argue that the majority of Capsule Network research has produced architectures that, while modestly divergent from the original Capsule Network, still retain a fundamentally similar structure. We posit that this inherent design similarity might be impeding the scalability of Capsule Networks. Our study contributes to the broader discussion on improving the robustness and scalability of Capsule Networks.
    


 
Comments:
9 pages, 7 figures


Subjects:

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

Cite as:
arXiv:2305.11178 [cs.CV]


 
(or 
arXiv:2305.11178v1 [cs.CV] for this version)
          
 
 

https://doi.org/10.48550/arXiv.2305.11178



Focus to learn more




                arXiv-issued DOI via DataCite
              







Submission history From: Georgios Leontidis [view email]       [v1]
        Sat, 13 May 2023 15:42:26 UTC (1,765 KB)



 

Full-text links:
Access Paper:


View a PDF of the paper titled Vanishing Activations: A Symptom of Deep Capsule Networks, by Miles Everett and 1 other authorsView PDFTeX SourceOther Formats
view license

 
    Current browse context: cs.CV


< prev

  |   
next >


new
 | 
recent
 | 2023-05

    Change to browse by:
    
cs
cs.LG




References & Citations

NASA ADSGoogle Scholar
Semantic Scholar




a
export BibTeX citation
Loading...




BibTeX formatted citation
×


loading...


Data provided by: 




Bookmark





 




Bibliographic Tools

Bibliographic and Citation Tools






Bibliographic Explorer Toggle



Bibliographic Explorer (What is the Explorer?)







Litmaps Toggle



Litmaps (What is Litmaps?)







scite.ai Toggle



scite Smart Citations (What are Smart Citations?)








Code, Data, Media

Code, Data and Media Associated with this Article






Links to Code Toggle



CatalyzeX Code Finder for Papers (What is CatalyzeX?)







DagsHub Toggle



DagsHub (What is DagsHub?)







GotitPub Toggle



Gotit.pub (What is GotitPub?)







Links to Code Toggle



Papers with Code (What is Papers with Code?)







ScienceCast Toggle



ScienceCast (What is ScienceCast?)











Demos

Demos






Replicate Toggle



Replicate (What is Replicate?)







Spaces Toggle



Hugging Face Spaces (What is Spaces?)







Spaces Toggle



TXYZ.AI (What is TXYZ.AI?)








Related Papers

Recommenders and Search Tools






Link to Influence Flower



Influence Flower (What are Influence Flowers?)







Connected Papers Toggle



Connected Papers (What is Connected Papers?)







Core recommender toggle



CORE Recommender (What is CORE?)





Author
Venue
Institution
Topic














        About arXivLabs
      



arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.










Which authors of this paper are endorsers? |
    Disable MathJax (What is MathJax?)
    












About
Help





contact arXivClick here to contact arXiv
 Contact


subscribe to arXiv mailingsClick here to subscribe
 Subscribe











Copyright
Privacy Policy




Web Accessibility Assistance


arXiv Operational Status 
                    Get status notifications via
                    email
                    or slack





 





