An adaptation of Confident Learning detects directional label errors in segmentation datasets without clean ground truth and leverages encoder feature separability to mitigate bias and equalize performance across subgroups.
De-biased representation learning for fairness with unreliable labels.arXiv preprint arXiv:2208.00651
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Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
An adaptation of Confident Learning detects directional label errors in segmentation datasets without clean ground truth and leverages encoder feature separability to mitigate bias and equalize performance across subgroups.