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 41–50 of 74 papers

TitleStatusHype
A Survey of Deep Visual Cross-Domain Few-Shot Learning—0
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey—0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—0
Task-aware Adaptive Learning for Cross-domain Few-shot Learning—0
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot LearningCode0
Feature Extractor Stacking for Cross-domain Few-shot LearningCode0
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning—0
Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains—0
A Framework of Meta Functional Learning for Regularising Knowledge Transfer—0
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot LearningCode0
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