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
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
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning—0
Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition—0
Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting—0
Ranking Distance Calibration for Cross-Domain Few-Shot Learning—0
Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning—0
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning—0
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