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 3140 of 74 papers

TitleStatusHype
Revisiting Prototypical Network for Cross Domain Few-Shot LearningCode1
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot LearningCode0
ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot LearningCode1
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
Feature Extractor Stacking for Cross-domain Few-shot LearningCode0
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning0
Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains0
Universal Representations: A Unified Look at Multiple Task and Domain LearningCode1
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