A Survey on Deep Transfer Learning

As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.

翻译：作为新的分类平台,深层次学习最近日益受到研究人员的注意,并成功地应用于许多领域,在生物信息学和机器人学等一些领域,由于数据采集和昂贵的注释费限制了数据开发,因此很难建造出一个有详细说明的大规模数据集。转让学习放宽了以下假设:培训数据必须与测试数据(即,d.)一样独立分配(即,d.),这促使我们利用转移学习解决培训数据不足的问题。这项调查侧重于审查目前通过使用深层神经网络及其应用进行的转移学习研究。我们界定了深层转移学习、分类,并审查了基于深层转移学习所用技术的近期研究工作。