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Domain Adaptation

1,518 평가 기록 170 datasets

Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.

하위 태스크

DG Domain Generalization 25 데이터셋 · 596 rows UD Unsupervised Domain Adaptation 49 데이터셋 · 393 rows UD Universal Domain Adaptation 4 데이터셋 · 51 rows SD Source-Free Domain Adaptation 15 데이터셋 · 41 rows PD Partial Domain Adaptation 5 데이터셋 · 28 rows BD Blended-target Domain Adaptation 3 데이터셋 · 6 rows CI Continuously Indexed Domain Adaptation 3 데이터셋 · 4 rows WU Wildly Unsupervised Domain Adaptation 4 데이터셋 · 4 rows OM Open-Set Multi-Target Domain Adaptation 3 데이터셋 · 3 rows TA Test-time Adaptation 1 데이터셋 · 1 rows OD Online Domain Adaptation 0 데이터셋 · 0 rows PZ Prompt-driven Zero-shot Domain Adaptation 0 데이터셋 · 0 rows VD Video Domain Adapation 0 데이터셋 · 0 rows