Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/JULE

JULE

Reported on 19 benchmarks across 1 task · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision19 results

Image ClusteringonStanford Cars
Accuracy· 2016-04-13
0.046
best: 0.646 (TURTLE (CLIP + DINOv2))
SOTA
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonStanford Cars
NMI· 2016-04-13
0.232
best: 0.354 (FineGAN)
SOTA
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonStanford Dogs
Accuracy· 2016-04-13
0.043
best: 0.079 (FineGAN)
SOTA
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonStanford Dogs
NMI· 2016-04-13
0.142
best: 0.233 (FineGAN)
SOTA
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonCUB Birds
Accuracy· 2016-04-13
0.044
best: 0.126 (FineGAN)
SOTA
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonCUB Birds
NMI· 2016-04-13
0.203
best: 0.403 (FineGAN)
SOTA
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonImageNet-10
Accuracy· 2016-04-13
0.3
best: 0.992 (TAC)
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonImageNet-10
NMI· 2016-04-13
0.175
best: 0.985 (TAC)
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonCIFAR-10
ARI· uses extra data· 2016-04-13
0.138
best: 0.989 (TURTLE (CLIP + DINOv2))
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonCIFAR-10
Accuracy· uses extra data· 2016-04-13
0.272
best: 0.995 (TURTLE (CLIP + DINOv2))
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonCIFAR-10
NMI· uses extra data· 2016-04-13
0.192
best: 0.985 (TURTLE (CLIP + DINOv2))
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonTiny-ImageNet
Accuracy· 2016-04-13
0.033
best: 0.698 (PRO-DSC)
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonTiny-ImageNet
NMI· 2016-04-13
0.102
best: 0.8178 (ITAE)
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonCIFAR-100
Accuracy· uses extra data· 2016-04-13
0.137
best: 0.898 (TURTLE (CLIP + DINOv2))
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonCIFAR-100
NMI· uses extra data· 2016-04-13
0.103
best: 0.915 (TURTLE (CLIP + DINOv2))
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonSTL-10
Accuracy· uses extra data· 2016-04-13
0.277
best: 0.997 (TURTLE (CLIP + DINOv2))
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonSTL-10
NMI· uses extra data· 2016-04-13
0.182
best: 0.993 (TURTLE (CLIP + DINOv2))
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonImagenet-dog-15
Accuracy· 2016-04-13
0.138
best: 0.943 (MAE-CT (best))
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628
Image ClusteringonImagenet-dog-15
NMI· 2016-04-13
0.054
best: 0.904 (MAE-CT (best))
Joint Unsupervised Learning of Deep Representations and Image Clusters arXiv:1604.03628