深度域适应综述:一般情况与复杂情况

A Review of Deep Domain Adaptation:General Situation and Complex Situation

信息时代产生的大量数据使机器学习技术成功地应用于许多领域.大多数机器学习技术需要满足训练集与测试集独立同分布的假设,但在实际应用中这个假设很难满足.域适应是一种在训练集和测试集不满足独立同分布条件下的机器学习技术.一般情况下的域适应只适用于源域目标域特征空间与标签空间都相同的情况,然而实际上这个条件很难满足.为了增强域适应技术的适用性,复杂情况下的域适应逐渐成为研究热点,其中标签空间不一致和复杂目标域情况下的域适应技术是近年来的新兴方向.随着深度学习技术的崛起,深度域适应已经成为域适应研究领域中的主流方法.本文对一般情况与复杂情况下的深度域适应的研究进展进行综述,对其缺点进行总结,并对其未来的发展趋势进行预测.首先对迁移学习相关概念进行介绍,然后分别对一般情况与复杂情况下的域适应、域适应技术的应用以及域适应方法性能的实验结果进行综述,最后对域适应领域的未来发展趋势进行展望并对全文内容进行总结.

The large amount of data generated in the information age enables machine learning to be successfully applied in many fields.Most machine learning techniques need to meet the assumption that the training set and the test set are independent and identically distributed,but in practice this assumption is difficult to meet.Domain adaptation is a machine learning technology in which the training set and test set do not need to satisfy the condition of independent and identical distribution.The general domain adaptation is only applicable to the case where feature space and label space of the source domain and target domain are the same,but in fact this condition is difficult to meet.In order to enhance the applicability of domain adaptation,domain adaptation under complex conditions has gradually become a research hotspot.Domain adaptation under the condition of inconsistent label space and complex target domain is an emerging direction in recent years.With the rise of deep learning technology,deep domain adaptation has become the mainstream method in the field of domain adaptation research.This article reviews the research progress of deep domain adaptation in general and complex situations,summarizes their shortcomings,and predicts their future development trends.This article firstly introduces the concepts of transfer learning,and then summarizes domain adaptation in general and complex situations,the application of domain adaptation technology and the performance of domain adaptation methods,finally prospects the development trend of the domain adaptation field and summarizes the content of the full text.