In transfer learning, transferability is one of the most fundamental problems, which aims to evaluate the effectiveness of arbitrary transfer tasks. Existing research focuses on classification tasks and neglects domain or task differences. More importantly, there is a lack of research to determine whether to transfer or not. To address these, we propose a new analytical approach and metric, Wasserstein Distance based Joint Estimation (WDJE), for transferability estimation and determination in a unified setting: classification and regression problems with domain and task differences. The WDJE facilitates decision-making on whether to transfer or not by comparing the target risk with and without transfer. To enable the comparison, we approximate the target transfer risk by proposing a non-symmetric, easy-to-understand and easy-to-calculate target risk bound that is workable even with limited target labels. The proposed bound relates the target risk to source model performance, domain and task differences based on Wasserstein distance. We also extend our bound into unsupervised settings and establish the generalization bound from finite empirical samples. Our experiments in image classification and remaining useful life regression prediction illustrate the effectiveness of the WDJE in determining whether to transfer or not, and the proposed bound in approximating the target transfer risk.

本文提出了一种新的分析方法和度量标准——基于Wasserstein距离的联合估计（WDJE），用于在统一的分类和回归问题中估计和确定可转移性，通过比较目标风险有无转移来促进决策，同时提出了一种容易理解和计算的目标风险上界，通过有限的目标标签关系目标风险与源模型性能、域和任务之间的Wasserstein距离关系，简化了目标风险的计算， 并将其推广到无监督设置中。实验结果表明，WDJE在图像分类和剩余寿命回归预测中的有效性，并且所提出的目标风险上界能够很好地估计目标转移风险。

是否转移：统一的可转移度度量和分析