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 21–30 of 74 papers

TitleStatusHype
Domain Adaptive Few-Shot Open-Set LearningCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
Cross Domain Few-Shot Learning via Meta Adversarial Training—0
A Survey of Deep Visual Cross-Domain Few-Shot Learning—0
From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning—0
Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning—0
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning—0
FrLove : Could a Frenchman rapidly identify Lovecraft?—0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—0
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?—0
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