Research map / Label-efficient and robust learning / Recognition and long-tail
Conditional Graphical Lasso for Multi-Label Image Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use image-conditioned label correlations instead of a shared label graph | CGL · MULANscene | Accuracy: 0.696 -> 0.705 | Helped |
| Use image-conditioned label correlations instead of a shared label graph | CGL · PASCAL07-PHOW | Accuracy: 0.327 -> 0.386 | Helped |
| Use image-conditioned label correlations instead of a shared label graph | CGL · PASCAL07-CNN | Accuracy: 0.634 -> 0.676 | Helped |
| Use image-conditioned label correlations instead of a shared label graph | CGL · PASCAL12-PHOW | Accuracy: 0.302 -> 0.365 | Helped |
| Use image-conditioned label correlations instead of a shared label graph | CGL · PASCAL12-CNN | Accuracy: 0.619 -> 0.661 | Helped |
| Increase λ2 to induce a sparser conditional label graph (ablation) | CGL · MULANscene and PASCAL07-CNN | number of graph edges | Hurt |
| Vary λ2 to retain relevant label correlations while increasing sparsity (ablation) | CGL · MULANscene and PASCAL07-CNN | classification performance | Mixed |
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