Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data

• URL: http://arxiv.org/abs/2107.11978v1
• Date: Mon, 26 Jul 2021 06:15:45 GMT
• Title: Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data
• Authors: Yuqian Fu, Yanwei Fu, Yu-Gang Jiang
• Abstract summary: A recent study finds that existing few-shot learning methods, trained on the source domain, fail to generalize to the novel target domain when a domain gap is observed. In this paper, we realize that the labeled target data in Cross-Domain Few-Shot Learning has not been leveraged in any way to help the learning process.
• Score: 95.47859525676246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A recent study finds that existing few-shot learning methods, trained on the source domain, fail to generalize to the novel target domain when a domain gap is observed. This motivates the task of Cross-Domain Few-Shot Learning (CD-FSL). In this paper, we realize that the labeled target data in CD-FSL has not been leveraged in any way to help the learning process. Thus, we advocate utilizing few labeled target data to guide the model learning. Technically, a novel meta-FDMixup network is proposed. We tackle this problem mainly from two aspects. Firstly, to utilize the source and the newly introduced target data of two different class sets, a mixup module is re-proposed and integrated into the meta-learning mechanism. Secondly, a novel disentangle module together with a domain classifier is proposed to extract the disentangled domain-irrelevant and domain-specific features. These two modules together enable our model to narrow the domain gap thus generalizing well to the target datasets. Additionally, a detailed feasibility and pilot study is conducted to reflect the intuitive understanding of CD-FSL under our new setting. Experimental results show the effectiveness of our new setting and the proposed method. Codes and models are available at https://github.com/lovelyqian/Meta-FDMixup.

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