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cross-domain few-shot learning
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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
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A Framework of Meta Functional Learning for Regularising Knowledge Transfer
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?
Cross Domain Few-Shot Learning via Meta Adversarial Training
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning
Ranking Distance Calibration for Cross-Domain Few-Shot Learning
Domain Agnostic Few-Shot Learning For Document Intelligence
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition
Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data
Cross-domain Few-shot Learning with Task-specific Adapters
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data
DAMSL: Domain Agnostic Meta Score-based Learning
Modular Adaptation for Cross-Domain Few-Shot Learning
Cross-domain few-shot learning with unlabelled data
Machine learning with limited data
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning
Cross-Domain Few-Shot Learning by Representation Fusion
Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition
Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data
A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning
Cross-Domain Few-Shot Learning with Meta Fine-Tuning
Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification
A Broader Study of Cross-Domain Few-Shot Learning
Self-Supervised Learning For Few-Shot Image Classification
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