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 51–60 of 74 papers

TitleStatusHype
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—0
FAD: Frequency Adaptation and Diversion for Cross-domain Few-shot Learning—0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification—0
FrLove : Could a Frenchman rapidly identify Lovecraft?—0
From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning—0
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?—0
Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains—0
Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning—0
Machine learning with limited data—0
MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning—0
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