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.
Detecting labeling bias using influence functions
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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.