DeepAI AI Chat
Log In Sign Up

Cross-domain few-shot learning with unlabelled data

by   Fupin Yao, et al.
University of Surrey

Few shot learning aims to solve the data scarcity problem. If there is a domain shift between the test set and the training set, their performance will decrease a lot. This setting is called Cross-domain few-shot learning. However, this is very challenging because the target domain is unseen during training. Thus we propose a new setting some unlabelled data from the target domain is provided, which can bridge the gap between the source domain and the target domain. A benchmark for this setting is constructed using DomainNet <cit.>. We come up with a self-supervised learning method to fully utilize the knowledge in the labeled training set and the unlabelled set. Extensive experiments show that our methods outperforms several baseline methods by a large margin. We also carefully design an episodic training pipeline which yields a significant performance boost.


Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition

The domain shift between the source and target domain is the main challe...

How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?

Cross-domain few-shot learning (CDFSL) remains a largely unsolved proble...

Cross-Domain Few-Shot Learning by Representation Fusion

In order to quickly adapt to new data, few-shot learning aims at learnin...

Enabling the Network to Surf the Internet

Few-shot learning is challenging due to the limited data and labels. Exi...

CDFSL-V: Cross-Domain Few-Shot Learning for Videos

Few-shot video action recognition is an effective approach to recognizin...

Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder

State of the art (SOTA) few-shot learning (FSL) methods suffer significa...

Selecting task with optimal transport self-supervised learning for few-shot classification

Few-Shot classification aims at solving problems that only a few samples...