计算机科学
元学习(计算机科学)
人工智能
深度学习
学会学习
公制(单位)
一般化
机器学习
光学(聚焦)
数据科学
任务(项目管理)
数学教育
数学分析
运营管理
物理
数学
管理
光学
经济
作者
Hassan Gharoun,Fereshteh Momenifar,Fang Chen,Amir H. Gandomi
摘要
Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction. Moreover, deep learning is notorious for poor generalization from few samples. Meta-learning is a promising approach that addresses these issues by adapting to new tasks with few-shot datasets. This survey first briefly introduces meta-learning and then investigates state-of-the-art meta-learning methods and recent advances in: (i) metric-based, (ii) memory-based, (iii), and learning-based methods. Finally, current challenges and insights for future researches are discussed.
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