Related papers: Cross-domain few-shot learning with unlabelled data

Cross-domain few-shot learning with unlabelled data

URL: http://arxiv.org/abs/2101.07899v1
Date: Tue, 19 Jan 2021 23:41:57 GMT
Title: Cross-domain few-shot learning with unlabelled data
Authors: Fupin Yao
Abstract summary: Few shot learning aims to solve the data scarcity problem. We propose a new setting some unlabelled data from the target domain is provided. We come up with a self-supervised learning method to fully utilize the knowledge in the labeled training set and the unlabelled set.
Score: 1.2183405753834562
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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 \cite{peng2018oment}. 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.

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