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
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
DAMSL: Domain Agnostic Meta Score-based LearningCode0
Modular Adaptation for Cross-Domain Few-Shot LearningCode0
Cross-domain few-shot learning with unlabelled data—0
Machine learning with limited data—0
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning—0
Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition—0
Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled DataCode0
A Transductive Multi-Head Model for Cross-Domain Few-Shot LearningCode0
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