DADG (AlexNet)

Reported on 4 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.

Methodology2 results

Domain AdaptationonPACS
Average Accuracy· 2020-11-01
72.11
best: 99 (SIMPLE+)
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation arXiv:2011.00444
Domain AdaptationonVLCS
Average Accuracy· 2020-11-01
74.46
best: 85.5 (CAR-FT (CLIP, ViT-B/16))
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation arXiv:2011.00444

Computer Vision2 results

Domain GeneralizationonPACS
Average Accuracy· 2020-11-01
72.11
best: 99 (SIMPLE+)
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation arXiv:2011.00444
Domain GeneralizationonVLCS
Average Accuracy· 2020-11-01
74.46
best: 85.5 (CAR-FT (CLIP, ViT-B/16))
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation arXiv:2011.00444