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.
That label’s got style: Handling label style bias for uncertain image segmentation.arXiv preprint arXiv:2303.15850
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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.