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/DBC

DBC

Reported on 8 benchmarks across 1 task · 1 paper · 3 SOTA

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

Computer Vision8 results

Image ClusteringonMNIST-full
Accuracy· 2017-03-23
0.976
best: 0.992 (SPC)
SOTA
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders arXiv:1703.07980
Image ClusteringonUSPS
Accuracy· 2017-03-23
0.743
best: 0.984 (SPC)
SOTA
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders arXiv:1703.07980
Image Clusteringoncoil-100
Accuracy· 2017-03-23
0.775
best: 0.984 (A-DSSC (Scattered))
SOTA
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders arXiv:1703.07980
Image ClusteringonMNIST-full
NMI· 2017-03-23
0.937
best: 0.975 (SPC)
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders arXiv:1703.07980
Image ClusteringonUSPS
NMI· 2017-03-23
0.724
best: 0.954 (SPC)
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders arXiv:1703.07980
Image ClusteringonCoil-20
Accuracy· 2017-03-23
0.793
best: 0.858 (AGDL)
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders arXiv:1703.07980
Image ClusteringonCoil-20
NMI· 2017-03-23
0.895
best: 1 (JULE-RC)
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders arXiv:1703.07980
Image Clusteringoncoil-100
NMI· 2017-03-23
0.905
best: 0.997 (A-DSSC (Scattered))
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders arXiv:1703.07980