A Review on Transferability Estimation in Deep Transfer Learning

Yihao Xue;Rui Yang;Xiaohan Chen;Weibo Liu;Zidong Wang;Xiaohui Liu
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Abstract

Deep transfer learning has become increasingly prevalent in various fields such as industry and medical science in recent years. To ensure the successful implementation of target tasks and improve the transfer performance, it is meaningful to prevent negative transfer. However, the dissimilarity between the data from source domain and target domain can pose challenges to transfer learning. Additionally, different transfer models exhibit significant variations in the performance of target tasks, potentially leading to a negative transfer phenomenon. To mitigate the adverse effects of the above factors, transferability estimation methods are employed in this field to evaluate the transferability of the data and the models of various deep transfer learning methods. These methods ascertain transferability by incorporating mutual information between the data or models of the source domain and the target domain. This article furnishes a comprehensive overview of four categories of transferability estimation methods in recent years. It employs qualitative analysis to evaluate various transferability estimation approaches, assisting researchers in selecting appropriate methods. Furthermore, this article evaluates the open problems associated with transferability estimation methods, proposing potential emerging areas for further research. Last, the open-source datasets commonly used in transferability estimation studies are summarized in this study.
深度迁移学习中可迁移性估计的研究进展
近年来,深度迁移学习在工业和医学等各个领域越来越普遍。为了保证目标任务的顺利实施,提高迁移绩效,防止负迁移具有重要意义。然而,源域和目标域数据的不相似性给迁移学习带来了挑战。此外,不同的迁移模型在目标任务的表现上表现出显著的差异,这可能导致负迁移现象。为了减轻上述因素的不利影响,该领域采用可转移性估计方法来评估各种深度迁移学习方法的数据和模型的可转移性。这些方法通过结合源域和目标域的数据或模型之间的相互信息来确定可移植性。本文对近年来的四类可转移性估计方法进行了综述。采用定性分析对各种可转移性估计方法进行评价,帮助研究者选择合适的方法。此外,本文评估了与可转移性估计方法相关的开放问题,提出了潜在的新兴领域,以进一步研究。最后,对可转移性估计研究中常用的开源数据集进行了总结。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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