论文标题
跨域通过元对抗训练几乎没有学习
Cross Domain Few-Shot Learning via Meta Adversarial Training
论文作者
论文摘要
几乎没有射击关系分类（RC）是机器学习的关键问题之一。当前的研究仅关注培训和测试来自同一领域的设置。但是，实际上，并非总是保证这个假设。在这项研究中，我们提出了一个新型模型，该模型考虑到上述跨域情况。不像以前的模型一样，我们仅使用源域数据来训练原型网络并在目标域数据上测试模型。提出了一个基于元的对抗训练框架（MBATF），以微调训练有素的网络，以适应来自目标域的数据。经验研究证实了所提出的模型的有效性。
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.
