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
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DOI: 10.4121/471ed486-bc86-40de-9e1d-9446cd6ec5aa
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
Licence CC BY 4.0
Interoperability
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.ResearchDataFormat
GitHub repository containing Python code.Associated peer-reviewed publication
Learning to Adapt to Position Bias in Vision Transformer ClassifiersReferences
Organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Computer Vision LabTo access the source code, use the following command:
git clone https://data.4tu.nl/v3/datasets/9c507af0-7860-45e7-b9bf-1ff14cdfe4a3.git