SOTAVerified

cross-domain few-shot learning

Its essence is transfer learning. The model needs to be trained in the source domain and then migrated to the target domain. Compliant with (1) the category in the target domain has never appeared in the source domain (2) the data distribution of the target domain is inconsistent with the source domain (3) each class in the target domain has very few labels

Papers

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Showing 41–50 of 74 papers

TitleStatusHype
Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning—0
A Framework of Meta Functional Learning for Regularising Knowledge Transfer—0
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning—0
A Survey of Deep Visual Cross-Domain Few-Shot Learning—0
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning—0
Cross Domain Few-Shot Learning via Meta Adversarial Training—0
Cross-domain few-shot learning with unlabelled data—0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning—0
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey—0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
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