同构迁移学习理论和算法研究进展
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摘要
迁移学习的目的是解决目标领域中训练样本不足的学习问题,可以把一些在其他相关的源领域中获得的知识,迁移到目标领域中.它放宽了传统机器学习中的两个基本假设:用于学习的训练样本与新的测试样本满足独立同分布的条件;必须有足够可利用的训练样本才能学习得到一个较好的分类模型.按照源领域和目标领域的特征空间是否相同可划分为同构迁移学习和异构迁移学习.本文主要针对同构迁移学习的相关研究进展进行了综述,从理论、算法、应用方面介绍了在该领域所做的研究工作,并指出了同构迁移学习未来可能的研究方向.
The goal of transfer learning is to solve the problem of insufficient training samples in the target domain.It can transfer the acquired knowledge from related source domain to the target domain.It relaxes two basic assumptions in traditional machine learning:the training samples and the new test samples satisfy the conditions of independent and identical distribution; furthermore,there must be enough training samples to learn a good classification model.According to whether the feature space of the source domain and the target domain are the same,it can be divided into homogeneous transfer learning and heterogeneous transfer learning.This paper mainly reviews the related research progress of homogeneous transfer learning,introduces the theory,algorithm and application of homogeneous transfer learning,and points out the hotissues of homogeneous transfer learning.
The goal of transfer learning is to solve the problem of insufficient training samples in the target domain.It can transfer the acquired knowledge from related source domain to the target domain.It relaxes two basic assumptions in traditional machine learning:the training samples and the new test samples satisfy the conditions of independent and identical distribution; furthermore,there must be enough training samples to learn a good classification model.According to whether the feature space of the source domain and the target domain are the same,it can be divided into homogeneous transfer learning and heterogeneous transfer learning.This paper mainly reviews the related research progress of homogeneous transfer learning,introduces the theory,algorithm and application of homogeneous transfer learning,and points out the hotissues of homogeneous transfer learning.
引文
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[33] Bengio Y.Deep learning of representations for unsupervised and transfer learning[C]//Proceedings of the International Conference on Machine Learning Workshop on Unsupervised and Transfer Learning,2012:17-36
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[40] Rozantsev A,Salzmann M,Fua P.Beyond sharing weights for deep domain adaptation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,41(4):801-814
[41] Chen W Y,Hsu T M,Tsai Y H.Transfer neural trees for heterogeneous domain adaptation[C]//Proceedings of the European Conference on Computer Vision,2016:399-414
[42] Saxena S,Verbeek J.Heterogeneous face recognition with CNNs[C]//Proceedings of the European Conference on Computer Vision Workshop on Transferring and Adapting Source Knowledge in Computer Vision,2016:483-491
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