Transfer learning is a popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. It has enjoyed numerous empirical successes and inspired a growing number of theoretical studies. This paper addresses the feasibility issue of transfer learning. It begins by establishing the necessary mathematical concepts and constructing a mathematical framework for transfer learning. It then identifies and formulates the three-step transfer learning procedure as an optimization problem, allowing for the resolution of the feasibility issue. Importantly, it demonstrates that under certain technical conditions, such as appropriate choice of loss functions and data sets, an optimal procedure for transfer learning exists. This study of the feasibility issue brings additional insights into various transfer learning problems. It sheds light on the impact of feature augmentation on model performance, explores potential extensions of domain adaptation, and examines the feasibility of efficient feature extractor transfer in image classification.

本文探讨了迁移学习的可行性问题，并构建了一个数学框架，将三个步骤的迁移学习过程形式化为一个优化问题，研究发现在适当的损失函数和数据集选择下，存在迁移学习的最佳过程，这些研究为特征增强在模型性能中的影响，域适应的潜在扩展以及在图像分类中进行高效特征提取器转移的可行性提供了新的见解。

迁移学习的可行性: 一个数学框架