FairDD uses domain-incremental replay, mixup, contrastive learning, and distillation to improve the accuracy-fairness tradeoff of dermatological classifiers on Fitzpatrick-17k and ISIC 2019.
Fairadabn: Miti- gating unfairness with adaptive batch normalization and its application to dermatological disease classification,
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FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis
FairDD uses domain-incremental replay, mixup, contrastive learning, and distillation to improve the accuracy-fairness tradeoff of dermatological classifiers on Fitzpatrick-17k and ISIC 2019.