Image Clustering
Models that partition the dataset into semantically meaningful clusters without having access to the ground truth labels.
Image credit: ImageNet clustering results of SCAN: Learning to Classify Images without Labels (ECCV 2020)
Papers
Showing 1–10 of 236 papers
All datasetsCIFAR-10CIFAR-100STL-10Imagenet-dog-15ImageNet-10MNIST-fullUSPSTiny ImageNetFashion-MNISTImageNetMNIST-testcoil-100
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | TURTLE (CLIP + DINOv2) | Accuracy | 1 | — | Unverified |
| 2 | PRCut (CLIP) | Accuracy | 0.98 | — | Unverified |
| 3 | PRO-DSC | Accuracy | 0.97 | — | Unverified |
| 4 | TEMI CLIP ViT-L (openai) | Accuracy | 0.97 | — | Unverified |
| 5 | DPAC | Accuracy | 0.93 | — | Unverified |
| 6 | SPICE-BPA | Accuracy | 0.93 | — | Unverified |
| 7 | SeCu | Accuracy | 0.93 | — | Unverified |
| 8 | TAC | Accuracy | 0.92 | — | Unverified |
| 9 | SPICE* | Accuracy | 0.92 | — | Unverified |
| 10 | DCN+BRB | Accuracy | 0.91 | — | Unverified |