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CMNIST

Dataset bias is a significant problem in training fair classifiers. When attributes unrelated to classification exhibit strong biases towards certain classes, classifiers trained on such dataset may overfit to these bias attributes, substantially reducing the accuracy for minority groups.

Data and Resources

Cite this as

Bo-Wei Huang, Keng-Te Liao, Chang-Sheng Kao, Shou-De Lin (2024). Dataset: CMNIST. https://doi.org/10.57702/k2qcbue3

DOI retrieved: December 16, 2024

Additional Info

Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.2308.08778
Citation
  • https://doi.org/10.48550/arXiv.2406.02889
  • https://doi.org/10.48550/arXiv.2301.13293
  • https://doi.org/10.48550/arXiv.2109.10432
  • https://doi.org/10.48550/arXiv.2106.03783
Author Bo-Wei Huang
More Authors
Keng-Te Liao
Chang-Sheng Kao
Shou-De Lin
Homepage https://www.kaggle.com/cmnist