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Data underlying the publication/thesis chapter: Scale Learning in Scale-Equivariant Convolutional Networks

Data underlying the publication/thesis chapter: Scale Learning in Scale-Equivariant Convolutional Networks

2
mentions
6
contributors

Description

VISAPP article and thesis chapter. This article addresses the mismatch between the scales for which scale equivariance methods are designed, and those occurring in real datasets.

Logo of Data underlying the publication/thesis chapter: Scale Learning in Scale-Equivariant Convolutional Networks
Keywords
computer vision
Convolutional neural network (CNN)
equivariance
scale
scale distribution
scale variation
Programming languages
  • Jupyter Notebook 70%
  • Python 20%
  • Markdown 4%
  • Other 3%
  • TeX 3%
  • YAML 1%
License
  • CC-BY-4.0
</>Source code
Packages
data.4tu.nl

Reference papers

Mentions

Contributors

JvG
Jan van Gemert
MB
Mark Basting
MK
Matthias Kümmerer
MB
Matthias Bethge
RB
Robert-Jan Bruintjes
TW
Thaddaüs Wiedemer

Member of community

4TU