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Models/GMDG (RegNetY-16GF)

GMDG (RegNetY-16GF)

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 AdaptationonOffice-Home
Average Accuracy· 2024-02-29
80.8
best: 90.6 (MoA (OpenCLIP, ViT-B/16))
Rethinking Multi-domain Generalization with A General Learning Objective arXiv:2402.18853
Domain AdaptationonDomainNet
Average Accuracy· 2024-02-29
54.6
best: 67.4 (L2C (CLIP, ViT-L/14))
Rethinking Multi-domain Generalization with A General Learning Objective arXiv:2402.18853
Domain AdaptationonVLCS
Average Accuracy· 2024-02-29
82.4
best: 85.5 (CAR-FT (CLIP, ViT-B/16))
Rethinking Multi-domain Generalization with A General Learning Objective arXiv:2402.18853
Domain AdaptationonTerraIncognita
Average Accuracy· 2024-02-29
60.7
best: 69.6 (UniDG + CORAL + ConvNeXt-B)
Rethinking Multi-domain Generalization with A General Learning Objective arXiv:2402.18853

Computer Vision4 results

Domain GeneralizationonOffice-Home
Average Accuracy· 2024-02-29
80.8
best: 90.6 (MoA (OpenCLIP, ViT-B/16))
Rethinking Multi-domain Generalization with A General Learning Objective arXiv:2402.18853
Domain GeneralizationonDomainNet
Average Accuracy· 2024-02-29
54.6
best: 67.4 (L2C (CLIP, ViT-L/14))
Rethinking Multi-domain Generalization with A General Learning Objective arXiv:2402.18853
Domain GeneralizationonVLCS
Average Accuracy· 2024-02-29
82.4
best: 85.5 (CAR-FT (CLIP, ViT-B/16))
Rethinking Multi-domain Generalization with A General Learning Objective arXiv:2402.18853
Domain GeneralizationonTerraIncognita
Average Accuracy· 2024-02-29
60.7
best: 69.6 (UniDG + CORAL + ConvNeXt-B)
Rethinking Multi-domain Generalization with A General Learning Objective arXiv:2402.18853