Semi Supervised Image Classification On Cifar 8
Métriques
Percentage error
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Percentage error |
|---|---|
| doublematch-improving-semi-supervised | 41.83± 1.22 |
| dash-semi-supervised-learning-with-dynamic | 44.83±1.36 |
| dp-ssl-towards-robust-semi-supervised | 43.17±1.29 |
| semireward-a-general-reward-model-for-semi | 15.62 |
| np-match-when-neural-processes-meet-semi | 38.67 |
| semantic-aware-representation-learning-via-1 | 42.38±2.52 |
| Modèle 7 | 40.25±0.95 |
| simmatch-semi-supervised-learning-with | 37.81 |
| class-aware-contrastive-semi-supervised | 38.81 |
| fixmatch-simplifying-semi-supervised-learning | 49.95±3.01 |
| flexmatch-boosting-semi-supervised-learning | 39.94±1.62 |
| contrastive-regularization-for-semi | 49.23 |
| shrinking-class-space-for-enhanced-certainty | 35.36 |
| usb-a-unified-semi-supervised-learning | 16.8 |
| dash-semi-supervised-learning-with-dynamic | 44.76±0.96 |
| remixmatch-semi-supervised-learning-with-1 | 44.28±2.06 |
| semantic-aware-representation-learning-via-1 | 35.75±0.53 |
| freematch-self-adaptive-thresholding-for-semi | 37.98 |