cross-domain few-shot learning
31 papers with code • 1 benchmarks • 1 datasets
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
Benchmarks
These leaderboards are used to track progress in cross-domain few-shot learning
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Office-Home
Latest papers with no code
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning
To enhance the transferability and facilitate fine-tuning, we introduce a simple yet effective approach to achieve long-range flattening of the minima in the loss landscape.
Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning
Due to the availability of only a few labeled instances for the novel target prediction task and the significant domain shift between the well annotated source domain and the target domain, cross-domain few-shot learning (CDFSL) induces a very challenging adaptation problem.
A Survey of Deep Visual Cross-Domain Few-Shot Learning
Research into Cross-Domain Few-Shot (CDFS) has emerged to address this issue, forming a more challenging and realistic setting.
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
Deep learning has been highly successful in computer vision with large amounts of labeled data, but struggles with limited labeled training data.
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning
Motivated by the observation that the domain shift between training tasks and target tasks usually can reflect in their style variation, we propose Task Augmented Meta-Learning (TAML) to conduct style transfer-based task augmentation to improve the domain generalization ability.
Task-aware Adaptive Learning for Cross-domain Few-shot Learning
In this paper, we first observe the dependence of task-specific parameter configuration on the target task.
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning
Cross-domain few-shot learning (CD-FSL), where there are few target samples under extreme differences between source and target domains, has recently attracted huge attention.
A Framework of Meta Functional Learning for Regularising Knowledge Transfer
The MFL computes meta-knowledge on functional regularisation generalisable to different learning tasks by which functional training on limited labelled data promotes more discriminative functions to be learned.
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?
In this paper, we investigate the role of self-supervised representation learning in the context of CDFSL via a thorough evaluation of existing methods.
Cross Domain Few-Shot Learning via Meta Adversarial Training
Few-shot relation classification (RC) is one of the critical problems in machine learning.
