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Models/SWD

SWD

Reported on 7 benchmarks across 4 tasks · 1 paper · 3 SOTA

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

Computer Vision2 results

Image-to-Image TranslationonSYNTHIA-to-Cityscapes
mIoU (13 classes)· 2019-03-10
48.1
best: 74.8 (HRDA + PiPa)
SOTA
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation arXiv:1903.04064
Image-to-Image TranslationonGTAV-to-Cityscapes Labels
mIoU· 2019-03-10
44.5
best: 77.7 (DCF)
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation arXiv:1903.04064

Medical2 results

Image GenerationonSYNTHIA-to-Cityscapes
mIoU (13 classes)· 2019-03-10
48.1
best: 74.8 (HRDA + PiPa)
SOTA
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation arXiv:1903.04064
Image GenerationonGTAV-to-Cityscapes Labels
mIoU· 2019-03-10
44.5
best: 77.7 (DCF)
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation arXiv:1903.04064

Miscellaneous2 results

1 Image, 2*2 StitchingonSYNTHIA-to-Cityscapes
mIoU (13 classes)· 2019-03-10
48.1
best: 74.8 (HRDA + PiPa)
SOTA
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation arXiv:1903.04064
1 Image, 2*2 StitchingonGTAV-to-Cityscapes Labels
mIoU· 2019-03-10
44.5
best: 77.7 (DCF)
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation arXiv:1903.04064

Methodology1 result

Domain AdaptationonVisDA2017
Accuracy· 2019-03-10
76.4
best: 93.8 (FFTAT)
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation arXiv:1903.04064