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ResearchFormerly WizWand

Image Classification on CIFAR-10B (α=0.8)

79.03AccuracyCOV
73.226874.733476.2477.7466Jul 7, 2026
Updated 4mo ago

Evaluation results

MethodLinks
COVFairness-aware training algorithm=COV2026.0779.0313.924.05
CKD2026.0778.492.857.3
Scratchloss=vanilla cross-entropy2026.0778.0117.927.46
COV+CPFairness-aware training algorithm=COV, CP regularization (+CP)=true2026.0777.744.319.42
MFD+augFairness-aware training algorithm=MFD, Counterfactual image augmentation (+aug)=true2026.0777.111.1614.79
LBC+CPFairness-aware training algorithm=LBC, CP regularization (+CP)=true2026.0776.883.0212.45
MFDFairness-aware training algorithm=MFD2026.0776.8412.2415.39
MFD+CPFairness-aware training algorithm=MFD, CP regularization (+CP)=true2026.0776.6710.0113.17
SS+CPFairness-aware training algorithm=SS, CP regularization (+CP)=true2026.0776.543.149.08
RWFairness-aware training algorithm=RW2026.0776.5312.1518.94
COV+augFairness-aware training algorithm=COV, Counterfactual image augmentation (+aug)=true2026.0776.528.1715.04
CKD (λ = 0)Lambda parameter (λ)=02026.0776.328.5911.23
LBCFairness-aware training algorithm=LBC2026.0776.1615.0117.12
RW+augFairness-aware training algorithm=RW, Counterfactual image augmentation (+aug)=true2026.0776.1512.9320.94
LBC+augFairness-aware training algorithm=LBC, Counterfactual image augmentation (+aug)=true2026.0775.829.0115.29
RW+CPFairness-aware training algorithm=RW, CP regularization (+CP)=true2026.0775.688.8313.92
CPCP regularization (+CP)=true2026.0775.262.0533.23
SSFairness-aware training algorithm=SS2026.0774.7716.4225.73
SS+augFairness-aware training algorithm=SS, Counterfactual image augmentation (+aug)=true2026.0773.459.9515.21