Multi-Instance Learning TPAUC Maximization on Fox (test)
0.578Mean TPAUCSONT (att)
Evaluation Results
| Method | Links | |
|---|---|---|
| SONT (att)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=attention-based2023.10 | 0.578 | |
| MIDAM (att)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=attention-based2023.10 | 0.509 | |
| SOTAS (att)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=attention-based2023.10 | 0.477 | |
| MIDAM (smx)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=stochastic smoothed-max2023.10 | 0.449 | |
| AUC-M (att)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=attention-based2023.10 | 0.444 | |
| SONT (att)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=attention-based2023.10 | 0.343 | |
| SOTAS (att)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=attention-based2023.10 | 0.278 | |
| MIDAM (smx)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=stochastic smoothed-max2023.10 | 0.265 | |
| AUC-M (att)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=attention-based2023.10 | 0.253 | |
| MIDAM (att)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=attention-based2023.10 | 0.249 | |
| SONT (att)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=attention-based2023.10 | 0.12 | |
| MIDAM (smx)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=stochastic smoothed-max2023.10 | 0.048 | |
| AUC-M (att)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=attention-based2023.10 | 0.032 | |
| SOTAS (att)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=attention-based2023.10 | 0.024 | |
| MIDAM (att)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=attention-based2023.10 | 0.016 |