Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

中文翻译：

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基于多目标优化的转移学习自动共享参数

转移学习方法利用不同数据集之间的相似性，通过将知识从源任务转移到目标任务来提高目标任务的性能。“转移什么”是转移学习中的主要研究问题。现有的迁移学习方法通​​常需要通过整合人类知识来获取共享参数。然而，在许多实际应用中，事先不知道可以共享哪些参数。转移学习模型本质上是一个特殊的多目标优化问题。因此，本文提出了一种基于多目标优化的转移学习自动共享参数技术，并通过多群粒子群优化器解决了优化问题。每个任务目标同时由子群优化。来自目标任务子群的当前最佳粒子用于指导源任务粒子的搜索，反之亦然。目标任务和源任务通过共享最佳粒子的信息共同解决，这是归纳偏差。通过对几种综合数据集以及学校数据集和地雷数据集的两个真实数据集进行实验评估，表明该算法是有效的。