ProtoFair uses prototype clustering to form pseudo-counterfactual pairs for an additive fairness contrastive loss that improves fairness metrics on CelebA and UTKFace while preserving accuracy in SimCLR and SupCon.
In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
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ProtoFair: Fair Self-Supervised Contrastive Learning via Pseudo-Counterfactual Pairs
ProtoFair uses prototype clustering to form pseudo-counterfactual pairs for an additive fairness contrastive loss that improves fairness metrics on CelebA and UTKFace while preserving accuracy in SimCLR and SupCon.