The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/965858bb-75e2-4fa5-a8bd-ffe0435fad0d

Bruintjes, Robert-Jan; Attila Lengyel; Kayhan, Osman; Zambrano, Davide; Tömen, Nergis et. al. (2025): Data underlying the publication: Data-Efficient Challenges in Visual Inductive Priors: A Retrospective. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/965858bb-75e2-4fa5-a8bd-ffe0435fad0d.v1

Categories

• Artificial Intelligence and Image Processing
• Information and Computing Sciences

Keywords

Computer Vision Convolutional neural network (CNN) data efficiency data efficient deep learning prior knowledge

Licence

CC BY 4.0

Interoperability

• RO-Crate Metadata

Export as...

RefWorks BibTeX Reference Manager Endnote DataCite NLM DC CFF

by Robert-Jan Bruintjes , Attila Lengyel , Osman Kayhan, Davide Zambrano , Nergis Tömen , Hadi Jamali-Rad , Jan van Gemert

Code used to create the article "Data-Efficient Challenges in Visual Inductive Priors: A Retrospective". This article summarizes the findings of four successive years of open challenges in the field of Computer Vision, aiming to stimulate research into data-efficient computer vision.

History

• 2025-09-09 first online, published, posted

Publisher

4TU.ResearchData

Format

GitHub repo, containing Python code and images.

Associated peer-reviewed publication

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective

References

• https://arxiv.org/abs/2506.08612

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Computer Vision Lab