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 51–60 of 74 papers

TitleStatusHype
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning—0
Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition—0
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning—0
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition—0
Task-aware Adaptive Learning for Cross-domain Few-shot Learning—0
Task-Specific Preconditioner for Cross-Domain Few-Shot Learning—0
Understanding the Cross-Domain Capabilities of Video-Based Few-Shot Action Recognition Models—0
A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (MedIMeta)—0
Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot LearningCode0
DAMSL: Domain Agnostic Meta Score-based LearningCode0
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