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
Exploring Cross-Domain Few-Shot Classification via Frequency-Aware PromptingCode0
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot LearningCode0
Self-Supervised Learning For Few-Shot Image ClassificationCode0
Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled DataCode0
Enhancing Masked Time-Series Modeling via Dropping PatchesCode0
Modular Adaptation for Cross-Domain Few-Shot LearningCode0
Feature Extractor Stacking for Cross-domain Few-shot LearningCode0
Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image ClassificationCode0
Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised AutoencoderCode0
Cross-Domain Few-Shot Learning via Adaptive Transformer NetworksCode0
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