Abstract
Transfer learning plays a vital role in improving model performance in data-scarce scenarios. However, naive uniform transfer from multiple source tasks may result in negative transfer, highlighting the need to properly balance the contributions of heterogeneous sources. Moreover, existing transfer learning methods typically focus on optimizing either the source weights or the amount of transferred samples, while largely neglecting the joint consideration of the other. In this work, we propose a theoretical framework, Unified Optimization of Weights and Quantities (UOWQ), which formulates multi-source transfer learning as a parameter estimation problem grounded in an asymptotic analysis of a Kullback-Leibler divergence-based generalization error measure. The proposed framework jointly determines the optimal source weights and optimal transfer quantities for each source task. Firstly, we prove that using all available source samples is always optimal once the weights are properly adjusted, and we provide a theoretical explanation for this phenomenon. Moreover, to determine the optimal transfer weights, our analysis yields closed-form solutions in the single-source setting and develops a convex optimization-based numerical procedure for the multi-source case. Building on the theoretical results, we further propose practical algorithms for both multi-source transfer learning and multi-task learning settings. Extensive experiments on real-world benchmarks, including DomainNet and Office-Home, demonstrate that UOWQ consistently outperforms strong baselines. The results validate both the theoretical predictions and the practical effectiveness of our framework.
Abstract (translated)
迁移学习在数据稀缺场景中提升模型性能方面扮演着至关重要的角色。然而，直接从多个源任务进行均匀迁移可能会导致负向迁移（即迁移效果变差），这表明需要恰当地平衡异质来源的贡献。此外，现有的迁移学习方法通常专注于优化源权重或转移样本的数量，却很少兼顾两者的联合考虑。在这项工作中，我们提出了一种理论框架——权重量化统一优化（Unified Optimization of Weights and Quantities, UOWQ），该框架将多源迁移学习形式化为一个基于Kullback-Leibler散度泛化误差度量的渐近分析参数估计问题。所提出的框架可以同时确定每个源任务的最佳权重和最佳转移数量。 首先，我们证明了只要权重调整得当，使用所有可用的源样本总是最优的选择，并提供了对此现象的理论解释。此外，为了确定最佳迁移权重，我们的分析在单一来源设置下给出了封闭形式解，在多来源情况下开发了一种基于凸优化的数值方法。 在此基础上，我们进一步为多源迁移学习和多任务学习场景提出了实用算法。我们在包括DomainNet和Office-Home在内的真实世界基准数据集上进行了广泛实验，结果显示UOWQ始终优于强大的基线模型。这些结果验证了我们的框架理论预测的有效性和其实用性。
URL
https://arxiv.org/abs/2601.10779
