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Models/MRNet

MRNet

Reported on 23 benchmarks across 6 tasks · 1 paper · 9 SOTA

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

Methodology16 results

Domain AdaptationonSYNTHIA-to-Cityscapes Labels
mIoU· 2019-12-24
46.5
SOTA
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164
Domain AdaptationonGTA5 to Cityscapes
mIoU· 2019-12-24
48.3
best: 77.8 (HALO)
SOTA
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164
Domain AdaptationonGTAV-to-Cityscapes Labels
mIoU· 2019-12-24
45.5
best: 75.9 (MIC)
SOTA
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164
Domain AdaptationonCityscapes-to-OxfordCar
mIoU· 2019-12-24
73.9
best: 75.2 (Uncertainty + Adaboost)
SOTA
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164
Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU (13 classes)· 2019-12-24
50.2
best: 75.9 (DCF)
SOTA
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164
Domain AdaptationonGTA5+Synscapes to Cityscapes
mIoU· 2019-12-24
47.6
best: 59 (MSCL)
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164
Domain AdaptationonGTAV+Synscapes to Cityscapes
mIoU· 2019-12-24
47.6
best: 69 (DDB)
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164
Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU· 2019-12-24
43.2
best: 78.1 (HALO)
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164
Multi-Label ClassificationonMRNet
AUC on ACL Tear (ACL)
0.915
Multi-Label ClassificationonMRNet
AUC on Abnormality (ABN)
0.944
Multi-Label ClassificationonMRNet
AUC on Meniscus Tear (MEN)
0.822
Multi-Label ClassificationonMRNet
Accuracy on ACL Tear (ACL)
0.867
Multi-Label ClassificationonMRNet
Accuracy on Abnormality (ABN)
0.85
best: 0.874 (SKIDv3)
Multi-Label ClassificationonMRNet
Accuracy on Meniscus Tear (MEN)
0.725
Multi-Label ClassificationonMRNet
Average AUC
0.894
Multi-Label ClassificationonMRNet
Average Accuracy
0.814

Other4 results

Unsupervised Domain AdaptationonGTAV-to-Cityscapes Labels
mIoU· 2019-12-24
45.5
best: 75.9 (MIC)
SOTA
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164
Unsupervised Domain AdaptationonCityscapes-to-OxfordCar
mIoU· 2019-12-24
73.9
best: 75.2 (Uncertainty + Adaboost)
SOTA
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164
Unsupervised Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU· 2019-12-24
43.2
best: 69.3 (DCF)
SOTA
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164
Unsupervised Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU (13 classes)· 2019-12-24
50.2
best: 75.9 (DCF)
SOTA
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164

Computer Vision1 result

Image-to-Image TranslationonGTAV-to-Cityscapes Labels
mIoU· 2019-12-24
48.3
best: 77.7 (DCF)
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164

Medical1 result

Image GenerationonGTAV-to-Cityscapes Labels
mIoU· 2019-12-24
48.3
best: 77.7 (DCF)
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164

Miscellaneous1 result

1 Image, 2*2 StitchingonGTAV-to-Cityscapes Labels
mIoU· 2019-12-24
48.3
best: 77.7 (DCF)
Unsupervised Scene Adaptation with Memory Regularization in vivo arXiv:1912.11164