Table 3.

Results on Digits-MLT.

Algorithm	F1-Score (by domain)		Accuracy (by domain)		Accuracy (by shot)
	Average	Worst	Average	Worst	Many	Medium	Few	Zero
IRM	0.079	0.031	0.147	0.101	0.278	0.003	0.036	− 1
DANN	0.646	0.612	0.649	0.614	0.761	0.557	0.508	− 1
CDANN	0.584	0.511	0.596	0.526	0.784	0.476	0.307	− 1
CORAL	0.605	0.538	0.622	0.562	0.815	0.517	0.298	− 1
MMD	0.029	0.028	0.094	0.088	0.080	0.000	0.269	− 1
Focal	0.506	0.423	0.537	0.466	0.774	0.362	0.207	− 1
CBLoss	0.488	0.359	0.515	0.389	0.715	0.422	0.151	− 1
LDAM	0.546	0.400	0.577	0.446	0.792	0.472	0.197	− 1
Bsoftmax	0.595	0.485	0.604	0.501	0.718	0.577	0.359	− 1
CRT(2-stage training)	0.531	0.451	0.562	0.490	0.787	0.448	0.171	− 1
BoDA	0.556	0.458	0.583	0.495	0.814	0.439	0.222	− 1
BoDA(2-stage training)	0.601	0.539	0.617	0.554	0.810	0.504	0.303	− 1
ERM	0.479	0.372	0.524	0.433	0.787	0.360	0.111	− 1
GroupDRO	0.474	0.444	0.522	0.495	0.806	0.342	0.084	− 1
Mixup	0.571	0.454	0.593	0.487	0.804	0.476	0.240	− 1
SagNet	0.565	0.521	0.594	0.552	0.837	0.472	0.172	− 1
MLDG	0.485	0.385	0.530	0.438	0.789	0.392	0.088	− 1
MTL	0.479	0.362	0.522	0.415	0.782	0.368	0.102	− 1
Fish	0.449	0.368	0.498	0.421	0.771	0.324	0.075	− 1
BRL(ours)	0.672	0.630	0.675	0.635	0.781	0.608	0.511	− 1
BRL(2-stage training)	0.674	0.633	0.679	0.639	0.791	0.620	0.488	− 1