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

Showing 1120 of 74 papers

TitleStatusHype
Self-Supervision Can Be a Good Few-Shot LearnerCode1
Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression RecognitionCode1
Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image ClassificationCode1
Universal Representations: A Unified Look at Multiple Task and Domain LearningCode1
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot DifficultyCode1
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
Shallow Bayesian Meta Learning for Real-World Few-Shot RecognitionCode1
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