Accuracy: 0.646
Papers with Code · CC BY-SA 4.0
Image Clustering
Models that partition the dataset into semantically meaningful clusters without having access to the ground truth labels.
<span style="color:grey; opacity: 0.6"> Image credit: ImageNet clustering results of SCAN: Learning to Classify Images without Labels (ECCV 2020) </span>
논문
NMI: 0.964Accuracy: 0.850
NMI: 1.000
Accuracy: 0.619NMI: 0.682
NMI: 0.939
Accuracy: 0.65
NMI: 0.8042
Accuracy: 0.791NMI: 0.758
Accuracy: 0.591NMI: 0.642
NMI: 0.945Accuracy: 0.902
Accuracy: 0.983
Accuracy: 0.612NMI: 0.637
Accuracy: 0.078NMI: 0.354
Accuracy: 0.627NMI: 0.66
Accuracy: 50.1NMI: 62.1ARI: 34.4
Accuracy: 0.635NMI: 0.71
Accuracy: 0.672NMI: 0.684
Accuracy: 0.628NMI: 0.644
Accuracy: 0.679NMI: 0.735
Accuracy: 0.716NMI: 0.710
NMI: 0.924Accuracy: 0.801