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
Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning—0
Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image ClassificationCode0
Domain Adaptive Few-Shot Open-Set LearningCode1
CDFSL-V: Cross-Domain Few-Shot Learning for VideosCode1
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
A Survey of Deep Visual Cross-Domain Few-Shot Learning—0
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey—0
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot LearningCode1
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—0
Task-aware Adaptive Learning for Cross-domain Few-shot Learning—0
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