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
Domain Agnostic Few-Shot Learning For Document Intelligence—0
MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning—0
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning—0
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition—0
Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised AutoencoderCode0
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target DataCode1
Cross-domain Few-shot Learning with Task-specific AdaptersCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
DAMSL: Domain Agnostic Meta Score-based LearningCode0
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