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 Framework of Meta Functional Learning for Regularising Knowledge Transfer—0
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot LearningCode0
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?—0
Cross Domain Few-Shot Learning via Meta Adversarial Training—0
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot DifficultyCode1
When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework—0
FrLove : Could a Frenchman rapidly identify Lovecraft?—0
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning—0
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning—0
Ranking Distance Calibration for Cross-Domain Few-Shot Learning—0
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