Ranking Distance Calibration for Cross-Domain Few-Shot Learning
Ranking Distance Calibration for Cross-Domain Few-Shot Learning
Recent progress in few-shot learning promotes a more realistic cross-domain setting, where the source and target datasets are in different domains. Due to the domain gap and disjoint label spaces between source and target datasets, their shared knowledge is extremely limited. This encourages us to explore more information in the …