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Domain Generalization on DomainBed (out-of-domain)

86.5VLCS AccuracyBOA
76.30878.95481.684.246Dec 20, 2022Jul 13, 2023Feb 4, 2024Aug 28, 2024Mar 22, 2025Oct 14, 2025May 8, 2026

Evaluation results

MethodLinks
BOAPre-training=SAM pretrained2026.0586.597.48660.360.278.1
SAGE2026.0585.597.487.161.163.478.9
GGA2026.0584.197.285.758.859.877.1
GAM2026.0583.696.485.555.359.576.1
IRM2026.0582.996.183.256.759.175.6
SAM2026.0582.996.685.456.259.876.2
GSAM2026.0582.996.685.655.459.876.1
SAGM2026.0582.896.885.25876.476.4
FISH2026.0582.796.9855859.175
DISAM2026.0582.797.185.457.359.876.5
CORAL2026.0582.696.483.857.559.876
CDANN2026.0582.496.582.955.658.475.2
MMD2026.0582.395.883.657.459.975.8
IIB2026.0582.396.584.258.258.676
RSC2026.0582.296.583.258.25975.8
ERM2026.0581.995.984.156.159.575.5
DANN2026.0581.896.3835658.475.1
MASS2025.1281.188.773.847.5—72.8
DARTSWAD=true2023.0280.388.971.951.347.267.9
GMoEK=62025.1280.288.174.248.5—72.8
GMoEK=82025.128088.274.247.2—72.4
GMoEK=42025.1279.888.273.547.8—72.3
DynMoEvariant=with Gshard Loss2025.1279.488.473.646.6—72
SWAD2023.0279.188.170.65046.566.9
SWADSelection=Loss-aware trajectory2022.1279.188.170.65046.566.9
Ensemble* of inter-trainingSelection=Uniform, inference overhead=functional ensembling2022.127989.272.751.147.267.8
CORAL2023.0278.886.268.747.641.564.5
CORALSelection=ID val2022.1278.886.268.747.641.564.6
Model ratatouilleSelection=Greedy2022.1278.790.573.449.247.767.9
DANN2023.0278.683.665.946.738.362.6
Model soups+ (DiWA runs)Selection=Uniform, averaging=all weights across 3 data splits2022.1278.68972.851.947.768
IRM2023.0278.583.564.347.633.961.6
DARTSWAD=false2023.0278.587.370.148.745.866.1
Deep ensembles*Selection=Uniform, inference overhead=functional ensembling2022.1278.587.670.849.247.766.8
Ensemble* (DiWA runs)Selection=Uniform, inference overhead=functional ensembling2022.1278.588.171.750.84767.2
Model soups (DiWA runs)Selection=Greedy2022.1278.58871.551.647.767.5
FusingSelection=ID val2022.1278.58871.546.744.465.8
Model ratatouilleSelection=Uniform2022.1278.589.573.151.847.568.1
Model soups (DiWA runs)Selection=Uniform2022.1278.488.772.151.447.467.6
VREX2023.0278.384.966.446.433.661.9
Model ratatouille†Selection=Uniform+, training budget=larger (60 weight sets)2022.1278.389.873.55247.768.3
MASelection=Uniform trajectory2022.1278.287.570.650.34666.5
Vanilla fine-tuning (DiWA runs)Selection=ID val2022.1278.185.969.450.444.365.6
SagNet2023.0277.886.368.148.640.364.2
Inter-trainingSelection=ID val2022.1277.78969.946.744.565.6
ARM2023.0277.685.164.845.535.561.7
ERM2023.0277.585.566.546.140.963.3
MMD2023.0277.584.666.342.223.458.8
CDANN2023.0277.582.665.845.838.362
Vanilla fine-tuningSelection=ID val2022.1277.585.566.546.140.963.3
Mixup2023.0277.484.668.147.939.263.4
MLDG2023.0277.284.966.847.741.263.6
MTL2023.0277.284.666.445.640.662.9
RSC2023.0277.185.265.546.638.962.7
GroupDRO2023.0276.784.46643.233.360.7