Cross Domain Few-Shot Learning via Meta Adversarial Training

• URL: http://arxiv.org/abs/2202.05713v1
• Date: Fri, 11 Feb 2022 15:52:29 GMT
• Title: Cross Domain Few-Shot Learning via Meta Adversarial Training
• Authors: Jirui Qi, Richong Zhang, Chune Li, Yongyi Mao
• Abstract summary: Few-shot relation classification (RC) is one of the critical problems in machine learning. We present a novel model that takes into consideration the afore-mentioned cross-domain situation. A meta-based adversarial training framework is proposed to fine-tune the trained networks for adapting to data from the target domain.
• Score: 34.383449283927014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot relation classification (RC) is one of the critical problems in machine learning. Current research merely focuses on the set-ups that both training and testing are from the same domain. However, in practice, this assumption is not always guaranteed. In this study, we present a novel model that takes into consideration the afore-mentioned cross-domain situation. Not like previous models, we only use the source domain data to train the prototypical networks and test the model on target domain data. A meta-based adversarial training framework (\textbf{MBATF}) is proposed to fine-tune the trained networks for adapting to data from the target domain. Empirical studies confirm the effectiveness of the proposed model.

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