Image Clustering On Imagenet 10
Métriques
Accuracy
NMI
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy | NMI |
|---|---|---|
| joint-unsupervised-learning-of-deep | 0.300 | 0.175 |
| representation-learning-for-clustering-via | 0.958 | 0.907 |
| deep-probability-aggregation-clustering | 0.97 | 0.925 |
| contrastive-hierarchical-clustering | 0.953 | 0.907 |
| auto-encoding-variational-bayes | 0.334 | 0.193 |
| spice-semantic-pseudo-labeling-for-image | 0.969 | 0.927 |
| twin-contrastive-learning-for-online | 0.895 | 0.875 |
| clustering-friendly-representation-learning-1 | 0.954 | 0.898 |
| multi-modal-deep-clustering-unsupervised | 0.811 | 0.719 |
| image-clustering-with-external-guidance | 0.992 | 0.985 |
| exploring-non-contrastive-representation-1 | 0.962 | 0.908 |
| contrastive-clustering | 0.893 | 0.859 |
| unsupervised-deep-embedding-for-clustering | 0.381 | 0.282 |
| c3-cross-instance-guided-contrastive | 0.942 | 0.905 |
| deep-comprehensive-correlation-mining-for | 0.71 | 0.608 |
| deep-adaptive-image-clustering | 0.527 | 0.394 |
| unsupervised-representation-learning-with-1 | 0.346 | 0.225 |
| exploring-non-contrastive-representation-1 | 0.956 | 0.896 |