SLM

Reported on 4 benchmarks across 1 task · 1 paper · 2 SOTA

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

Methodology4 results

Domain AdaptationonOffice-31
Accuracy (%)· 2020-12-06
98.38
SOTA
Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation arXiv:2012.03358
Domain AdaptationonVisDA2017
Accuracy (%)· 2020-12-06
84.61
best: 87.69 (SPDA)
SOTA
Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation arXiv:2012.03358
Domain AdaptationonOffice-Home
Accuracy (%)· 2020-12-06
75.29
best: 80.2 (EvoADA)
Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation arXiv:2012.03358
Domain AdaptationonImageNet-Caltech
Accuracy (%)· 2020-12-06
81.86
best: 84.69 (AR)
Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation arXiv:2012.03358