点击次数：

论文类型：会议论文

发表时间：2019-04-14

收录刊物：EI

卷号：11440 LNAI

页面范围：305-317

摘要：One shot learning is a task of learning from a few examples, which poses a great challenge for current machine learning algorithms. One of the most effective approaches for one shot learning is metric learning. But metric-based approaches suffer from data shortage problem in one shot scenario. To alleviate this problem, we propose one shot learning with margin. The margin is beneficial to learn a more discriminative metric space. We integrate the margin into two representative one shot learning models, prototypical networks and matching networks, to enhance their generalization ability. Experimental results on benchmark datasets show that margin effectively boosts the performance of one shot learning models. © Springer Nature Switzerland AG 2019.