Table 1.

Results on classical MIL datasets

Methods	Musk1	Musk2	Fox	Tiger	Elephant
mi-Net [51]	0.889 ± 0.039	0.858 ± 0.049	0.613 ± 0.035	0.824 ± 0.034	0.858 ± 0.037
MI-Net [51]	0.887 ± 0.041	0.859 ± 0.046	0.622 ± 0.038	0.830 ± 0.032	0.862 ± 0.034
MI-Net with DS [51]	0.894 ± 0.042	0.874 ± 0.043	0.630 ± 0.037	0.845 ± 0.039	0.872 ± 0.032
MI-Net with RC [51]	0.898 ± 0.043	0.873 ± 0.044	0.619 ± 0.047	0.836 ± 0.037	0.873 ± 0.044
Attention [8]	0.892 ± 0.040	0.858 ± 0.048	0.615 ± 0.043	0.839 ± 0.022	0.868 ± 0.022
Gated Attention [8]	0.900 ± 0.050	0.863 ± 0.042	0.603 ± 0.029	0.845 ± 0.018	0.857 ± 0.027
mi-Net Attention [52]	0.900 ± 0.063	0.870 ± 0.048	0.630 ± 0.026	0.845 ± 0.028	0.865 ± 0.024
ELDB [53]	0.902 ± 0.016	0.857 ± 0.039	0.648 ± 0.014	0.767 ± 0.013	0.843 ± 0.012
TGA-MIL (ours)	0.910 ± 0.033	0.881 ± 0.040	0.628 ± 0.020	0.846 ± 0.015	0.875 ± 0.020

Experiments were repeated five times, with the average classification accuracy (±standard error) provided. The best results for each dataset are highlighted in bold