迁移学习述评
A Concise Review of Transfer Learning
摘要：
近年来大量标记数据的可用性导致研究人员引入了一种称为迁移学习的方法，该方法在收集新注释数据有困难的情况下利用现有数据。迁移学习旨在通过应用另一个相关的源数据来提高目标学习者的成绩。与传统的机器学习和数据挖掘技术不同，传统的机器学习和数据挖掘技术假设训练和测试数据来自相同的特征空间和分布，迁移学习可以处理域和分布之间存在差异的情况。这些特性使模型能够利用可用的相关源数据，并将底层知识扩展到目标任务，从而获得更好的性能。本文旨在对传统和当前的迁移学习环境、现有挑战和相关方法进行简要回顾。
英文摘要：
The availability of abundant labeled data in recent years led the researchers to introduce a methodology called transfer learning, which utilizes existing data in situations where there are difficulties in collecting new annotated data. Transfer learning aims to boost the performance of a target learner by applying another related source data. In contrast to the traditional machine learning and data mining techniques, which assume that the training and testing data lie from the same feature space and distribution, transfer learning can handle situations where there is a discrepancy between domains and distributions. These characteristics give the model the potential to utilize the available related source data and extend the underlying knowledge to the target task achieving better performance. This survey paper aims to give a concise review of traditional and current transfer learning settings, existing challenges, and related approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
