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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.

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Cite this as

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

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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