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Domain Generalization on DomainBed (out-of-domain)

81.1VLCS Accuracy

MASS

Updated 4d ago

Evaluation Results

Method
MASS 2025.12	81.1	88.7	73.8	47.5	-	72.8
DART 2023.02	80.3	88.9	71.9	51.3	47.2	67.9
GMoE 2025.12	80.2	88.1	74.2	48.5	-	72.8
GMoE 2025.12	80	88.2	74.2	47.2	-	72.4
GMoE 2025.12	79.8	88.2	73.5	47.8	-	72.3
DynMoE 2025.12	79.4	88.4	73.6	46.6	-	72
SWAD 2023.02	79.1	88.1	70.6	50	46.5	66.9
SWAD 2022.12	79.1	88.1	70.6	50	46.5	66.9
Ensemble* of inter-training 2022.12	79	89.2	72.7	51.1	47.2	67.8
CORAL 2023.02	78.8	86.2	68.7	47.6	41.5	64.5
CORAL 2022.12	78.8	86.2	68.7	47.6	41.5	64.6
Model ratatouille 2022.12	78.7	90.5	73.4	49.2	47.7	67.9
DANN 2023.02	78.6	83.6	65.9	46.7	38.3	62.6
Model soups+ (DiWA runs) 2022.12	78.6	89	72.8	51.9	47.7	68
IRM 2023.02	78.5	83.5	64.3	47.6	33.9	61.6
DART 2023.02	78.5	87.3	70.1	48.7	45.8	66.1
Deep ensembles* 2022.12	78.5	87.6	70.8	49.2	47.7	66.8
Ensemble* (DiWA runs) 2022.12	78.5	88.1	71.7	50.8	47	67.2
Model soups (DiWA runs) 2022.12	78.5	88	71.5	51.6	47.7	67.5
Fusing 2022.12	78.5	88	71.5	46.7	44.4	65.8
Model ratatouille 2022.12	78.5	89.5	73.1	51.8	47.5	68.1
Model soups (DiWA runs) 2022.12	78.4	88.7	72.1	51.4	47.4	67.6
VREX 2023.02	78.3	84.9	66.4	46.4	33.6	61.9
Model ratatouille† 2022.12	78.3	89.8	73.5	52	47.7	68.3
MA 2022.12	78.2	87.5	70.6	50.3	46	66.5
Vanilla fine-tuning (DiWA runs) 2022.12	78.1	85.9	69.4	50.4	44.3	65.6
SagNet 2023.02	77.8	86.3	68.1	48.6	40.3	64.2
Inter-training 2022.12	77.7	89	69.9	46.7	44.5	65.6
ARM 2023.02	77.6	85.1	64.8	45.5	35.5	61.7
ERM 2023.02	77.5	85.5	66.5	46.1	40.9	63.3
MMD 2023.02	77.5	84.6	66.3	42.2	23.4	58.8
CDANN 2023.02	77.5	82.6	65.8	45.8	38.3	62
Vanilla fine-tuning 2022.12	77.5	85.5	66.5	46.1	40.9	63.3
Mixup 2023.02	77.4	84.6	68.1	47.9	39.2	63.4
MLDG 2023.02	77.2	84.9	66.8	47.7	41.2	63.6
MTL 2023.02	77.2	84.6	66.4	45.6	40.6	62.9
RSC 2023.02	77.1	85.2	65.5	46.6	38.9	62.7
GroupDRO 2023.02	76.7	84.4	66	43.2	33.3	60.7