Unsupervised Image Classification On Cifar 20
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy |
|---|---|
| unsupervised-visual-representation-learning-3 | 49.7 |
| breaking-the-reclustering-barrier-in-centroid | 55.43 |
| mv-mr-multi-views-and-multi-representations | 73.2 |
| breaking-the-reclustering-barrier-in-centroid | 56.92 |
| breaking-the-reclustering-barrier-in-centroid | 50.46 |
| exploring-a-principled-framework-for-deep | 71.6 |
| learning-to-classify-images-without-labels | 50.7 |
| stable-cluster-discrimination-for-deep-1 | 55.2 |
| improving-unsupervised-image-clustering-with | 54.3 |
| mitigating-embedding-and-class-assignment | 35.3 |
| scatsimclr-self-supervised-contrastive | 63.76 |
| Modèle 12 | 55.5 |
| loss-function-entropy-regularization-for | 56.1 |
| invariant-information-distillation-for | 25.7 |