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Models/DREAME

DREAME

Reported on 8 benchmarks across 2 tasks · 1 paper

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

Methodology4 results

Domain AdaptationonPACS
Average Accuracy· 2021-12-17
87.35
best: 99 (SIMPLE+)
Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization arXiv:2112.09802
Domain AdaptationonOffice-Home
Average Accuracy· 2021-12-17
69.76
best: 90.6 (MoA (OpenCLIP, ViT-B/16))
Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization arXiv:2112.09802
Domain AdaptationonVLCS
Average Accuracy· 2021-12-17
79.02
best: 85.5 (CAR-FT (CLIP, ViT-B/16))
Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization arXiv:2112.09802
Domain AdaptationonTerraIncognita
Average Accuracy· 2021-12-17
48.66
best: 69.6 (UniDG + CORAL + ConvNeXt-B)
Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization arXiv:2112.09802

Computer Vision4 results

Domain GeneralizationonPACS
Average Accuracy· 2021-12-17
87.35
best: 99 (SIMPLE+)
Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization arXiv:2112.09802
Domain GeneralizationonOffice-Home
Average Accuracy· 2021-12-17
69.76
best: 90.6 (MoA (OpenCLIP, ViT-B/16))
Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization arXiv:2112.09802
Domain GeneralizationonVLCS
Average Accuracy· 2021-12-17
79.02
best: 85.5 (CAR-FT (CLIP, ViT-B/16))
Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization arXiv:2112.09802
Domain GeneralizationonTerraIncognita
Average Accuracy· 2021-12-17
48.66
best: 69.6 (UniDG + CORAL + ConvNeXt-B)
Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization arXiv:2112.09802