Data underlying the publication/thesis chapter: Learning to Adapt to Position Bias in Vision Transformer Classifiers

DOI:10.4121/471ed486-bc86-40de-9e1d-9446cd6ec5aa.v1
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: 10.4121/471ed486-bc86-40de-9e1d-9446cd6ec5aa

Datacite citation style

Bruintjes, Robert-Jan; van Gemert, Jan (2025): Data underlying the publication/thesis chapter: Learning to Adapt to Position Bias in Vision Transformer Classifiers. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/471ed486-bc86-40de-9e1d-9446cd6ec5aa.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Software

This article proposes a novel method to measure learned position bias in Vision Transformers, and investigates which model design choices affect learned equivariance.

History

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

Publisher

4TU.ResearchData

Format

GitHub repository containing Python code.

Organizations

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

To access the source code, use the following command:

git clone https://data.4tu.nl/v3/datasets/9c507af0-7860-45e7-b9bf-1ff14cdfe4a3.git

Or download the latest commit as a ZIP.