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

Showing 1120 of 74 papers

TitleStatusHype
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Cross-Domain Few-Shot Learning by Representation FusionCode1
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
Revisiting Prototypical Network for Cross Domain Few-Shot LearningCode1
Domain Adaptive Few-Shot Open-Set LearningCode1
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot LearningCode1
Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot LearningCode1
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot LearningCode1
CDFSL-V: Cross-Domain Few-Shot Learning for VideosCode1
Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot LearningCode1
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