Table 5.

Results on PACS-MLT.

Algorithm	F1-Score (by domain)		Accuracy (by domain)		Accuracy (by shot)
	Average	Worst	Average	Worst	Many	Medium	Few	Zero
IRM	0.964	0.949	0.964	0.949	0.961	0.980	1.000	− 1
DANN	0.930	0.902	0.928	0.897	0.922	0.980	0.960	− 1
CDANN	0.927	0.908	0.926	0.909	0.924	0.950	0.920	− 1
CORAL	0.984	0.977	0.984	0.977	0.982	1.000	1.000	− 1
MMD	0.975	0.960	0.975	0.960	0.974	0.990	0.960	− 1
Focal	0.981	0.963	0.981	0.963	0.981	0.980	0.980	− 1
CBLoss	0.979	0.971	0.979	0.971	0.978	1.000	0.980	− 1
LDAM	0.979	0.971	0.979	0.971	0.978	1.000	0.940	− 1
Bsoftmax	0.980	0.966	0.979	0.966	0.978	1.000	0.980	− 1
CRT(2-stage training)	0.984	0.972	0.984	0.971	0.984	0.980	0.980	− 1
BoDA	0.979	0.969	0.979	0.969	0.978	0.990	1.000	− 1
BoDA(2-stage training)	0.982	0.974	0.982	0.974	0.980	1.000	1.000	− 1
ERM	0.981	0.966	0.981	0.966	0.982	0.980	0.960	− 1
GroupDRO	0.982	0.972	0.981	0.971	0.981	0.980	1.000	− 1
Mixup	0.981	0.963	0.981	0.963	0.980	0.990	0.980	− 1
SagNet	0.976	0.963	0.976	0.963	0.974	1.000	1.000	− 1
MLDG	0.982	0.977	0.982	0.977	0.980	1.000	1.000	− 1
MTL	0.980	0.963	0.980	0.963	0.979	0.990	0.980	− 1
Fish	0.979	0.969	0.979	0.969	0.980	0.980	0.960	− 1
BRL(ours)	0.979	0.968	0.979	0.969	0.977	1.000	1.000	− 1
BRL(2-stage training)	0.982	0.977	0.982	0.977	0.980	1.000	1.000	− 1