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 61–70 of 74 papers

TitleStatusHype
Modular Adaptation for Cross-Domain Few-Shot LearningCode0
Cross-domain few-shot learning with unlabelled data—0
Machine learning with limited data—0
Shallow Bayesian Meta Learning for Real-World Few-Shot RecognitionCode1
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning—0
Cross-Domain Few-Shot Learning by Representation FusionCode1
Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition—0
A Transductive Multi-Head Model for Cross-Domain Few-Shot LearningCode0
Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled DataCode0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning—0
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