Table 8.

Results on DomainNet-MLT.

Algorithm	F1-Score (by domain)		Accuracy (by domain)		Accuracy (by shot)
	Average	Worst	Average	Worst	Many	Medium	Few	Zero
IRM	0.136	0.032	0.169	0.057	0.205	0.128	0.061	0.063
DANN	0.538	0.311	0.551	0.305	0.601	0.540	0.389	0.318
CDANN	0.554	0.327	0.565	0.318	0.623	0.543	0.394	0.325
CORAL	0.597	0.340	0.606	0.326	0.672	0.587	0.407	0.320
MMD	0.002	0.001	0.003	0.002	0.003	0.003	0.002	0.003
Focal	0.578	0.302	0.587	0.293	0.657	0.572	0.369	0.279
CBLoss	0.588	0.318	0.599	0.321	0.651	0.618	0.439	0.298
LDAM	0.586	0.307	0.597	0.301	0.666	0.582	0.389	0.288
Bsoftmax	0.605	0.344	0.613	0.340	0.658	0.615	0.482	0.386
CRT (2-stage training)	0.617	0.340	0.629	0.346	0.685	0.647	0.457	0.313
BoDA	0.597	0.348	0.607	0.337	0.669	0.592	0.413	0.336
BoDA (2-stage training)	0.620	0.366	0.632	0.365	0.683	0.640	0.492	0.350
ERM	0.586	0.307	0.596	0.300	0.666	0.579	0.383	0.283
GroupDRO	0.536	0.297	0.550	0.302	0.622	0.521	0.357	0.239
Mixup	0.583	0.329	0.595	0.323	0.660	0.578	0.401	0.306
SagNet	0.588	0.312	0.598	0.305	0.668	0.581	0.373	0.291
MLDG	0.589	0.314	0.600	0.310	0.667	0.583	0.392	0.298
MTL	0.584	0.311	0.593	0.306	0.665	0.571	0.373	0.288
Fish	0.588	0.317	0.600	0.313	0.668	0.583	0.397	0.291
BRL (ours)	0.569	0.361	0.583	0.362	0.611	0.614	0.483	0.377
BRL (2-stage training)	0.588	0.370	0.602	0.372	0.636	0.631	0.504	0.365