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 2130 of 74 papers

TitleStatusHype
Domain Adaptive Few-Shot Open-Set LearningCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
Modular Adaptation for Cross-Domain Few-Shot LearningCode0
Cross-Domain Few-Shot Learning via Adaptive Transformer NetworksCode0
Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image ClassificationCode0
Enhancing Masked Time-Series Modeling via Dropping PatchesCode0
Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled DataCode0
Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot LearningCode0
Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot LearningCode0
DAMSL: Domain Agnostic Meta Score-based LearningCode0
Show:102550
← PrevPage 3 of 8Next →

No leaderboard results yet.