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arXiv · 2202.05713

Cross Domain Few-Shot Learning via Meta Adversarial Training

Jirui Qi·Richong Zhang·Chune Li·Yongyi Mao
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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 (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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Jirui Qi, Richong Zhang, Chune Li, Yongyi Mao. 2022-02-11. Cross Domain Few-Shot Learning via Meta Adversarial Training. https://arxiv.org/abs/2202.05713

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