Semi-supervised Image Classification on CUB200 (10% train)
81.23AccuracyDGCG
Evaluation Results
| Method | Links | |
|---|---|---|
| DGCGFeatures=ViT-B162026.05 | 81.23 | |
| MGCNFeatures=ViT-B162026.05 | 79.27 | |
| WSEFFeatures=ViT-B162026.05 | 78.64 | |
| GRaNDeFeatures=ViT-B162026.05 | 78.58 | |
| SVMFeatures=ViT-B162026.05 | 75.61 | |
| OPFFeatures=ViT-B162026.05 | 73.27 | |
| LS+ SVMFeatures=ViT-B162026.05 | 66.81 | |
| LS+ OPFFeatures=ViT-B162026.05 | 66.68 | |
| LS+ kNNFeatures=ViT-B162026.05 | 66.15 | |
| LS+ ML-Perc.Features=ViT-B162026.05 | 62.81 | |
| GRaNDeFeatures=ResNet2026.05 | 54.58 | |
| DGCGFeatures=ResNet2026.05 | 54.43 | |
| MGCNFeatures=ResNet2026.05 | 52.85 | |
| GNN-LDSFeatures=ViT-B162026.05 | 52.42 | |
| WSEFFeatures=ResNet2026.05 | 52.17 | |
| SVMFeatures=ResNet2026.05 | 48.84 | |
| GRaNDeFeatures=SENet2026.05 | 43.78 | |
| DGCGFeatures=SENet2026.05 | 41 | |
| MGCNFeatures=SENet2026.05 | 40.31 | |
| LS+ ML-Perc.Features=ResNet2026.05 | 39.68 | |
| LS+ OPFFeatures=ResNet2026.05 | 39.28 | |
| LS+ SVMFeatures=ResNet2026.05 | 38.7 | |
| OPFFeatures=ResNet2026.05 | 38.59 | |
| CoMatch2026.05 | 38.29 | |
| GNN-LDSFeatures=ResNet2026.05 | 37.78 | |
| LS+ kNNFeatures=ResNet2026.05 | 36.99 | |
| WSEFFeatures=SENet2026.05 | 36.49 | |
| SVMFeatures=SENet2026.05 | 35.32 | |
| ML-Perc.Features=ResNet2026.05 | 32.24 | |
| ML-Perc.Features=SENet2026.05 | 32.15 | |
| OPFFeatures=SENet2026.05 | 30.94 | |
| PL+ SGDFeatures=ViT-B162026.05 | 30.19 | |
| LS+ ML-Perc.Features=SENet2026.05 | 25.72 | |
| LS+ OPFFeatures=SENet2026.05 | 25.38 | |
| LS+ SVMFeatures=SENet2026.05 | 24.82 | |
| PL+ SGDFeatures=ResNet2026.05 | 21.67 | |
| PL+ SGDFeatures=SENet2026.05 | 20.96 | |
| LS+ kNNFeatures=SENet2026.05 | 20 | |
| ML-Perc.Features=ViT-B162026.05 | 12.02 |