Image Clustering On Tiny Imagenet
Métriques
Accuracy
NMI
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy | NMI |
|---|---|---|
| deep-adaptive-image-clustering | 0.066 | 0.190 |
| multi-modal-deep-clustering-unsupervised | 0.119 | 0.274 |
| unsupervised-representation-learning-with-1 | 0.041 | 0.135 |
| exploring-a-principled-framework-for-deep | 0.698 | 0.805 |
| auto-encoding-variational-bayes | 0.036 | 0.113 |
| spice-semantic-pseudo-labeling-for-image | 0.305 | 0.449 |
| improving-image-clustering-with-artifacts | 0.6823 | 0.8178 |
| unsupervised-deep-embedding-for-clustering | 0.037 | 0.115 |
| contrastive-clustering | 0.14 | 0.34 |
| c3-cross-instance-guided-contrastive | 0.141 | 0.335 |
| information-maximization-clustering-via-multi | 0.282 | 0.526 |
| deep-comprehensive-correlation-mining-for | 0.108 | 0.224 |
| joint-unsupervised-learning-of-deep | 0.033 | 0.102 |
| information-maximization-clustering-via-multi | 0.279 | 0.485 |