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Models/HALO

HALO

Reported on 6 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.

Methodology4 results

Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU· 2023-06-19
78.1
SOTA
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift arXiv:2306.11180
Domain AdaptationonGTA5 to Cityscapes
mIoU· uses extra data· 2023-06-19
77.8
SOTA
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift arXiv:2306.11180
Domain AdaptationonCityscapes to ACDC
mIoU· 2023-06-19
71.9
best: 78.8 (SoRA)
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift arXiv:2306.11180
Domain AdaptationonGTA5 to Cityscapes
mIoU· 2023-06-19
73.3
best: 77.8
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift arXiv:2306.11180

Computer Vision1 result

Source-Free Domain AdaptationonGTA5 to Cityscapes
mIoU· 2023-06-19
73.3
SOTA
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift arXiv:2306.11180

Medical1 result

Semantic SegmentationonCityscapes val
mIoU· 2023-06-19
77.8
best: 90.3 (EfficientPS (Cityscapes-fine))
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift arXiv:2306.11180

Audio1 result

10-shot image generationonCityscapes val
mIoU· 2023-06-19
77.8
best: 90.3 (EfficientPS (Cityscapes-fine))
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift arXiv:2306.11180