The doi 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: 10.4121/8648064e-aa7b-4a09-a755-7eb2d90bef66

Tepeli, Yasin; Gonçalves, Joana (2024): Data and code underlying the publication: DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/8648064e-aa7b-4a09-a755-7eb2d90bef66.v1

Dataset

• Artificial Intelligence and Image Processing
• Information and Computing Sciences

selection bias, self training, semi supervised, unlabeled data

GPL-3.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Yasin Tepeli , Joana Gonçalves

This repository consists of Data/Code to reproduce the results of the thesis chapter "DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning".

The data is shared at: https://doi.org/10.6084/m9.figshare.27003601

The code is shared at: https://github.com/joanagoncalveslab/DCAST

• 2024-11-25 first online, published, posted

4TU.ResearchData

DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning

https://doi.org/10.6084/m9.figshare.27868476

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Delft Bioinformatics La