Auto-sharing parameters for transfer learning based on multi-objective optimization

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hai-Lin Liu, Fangqing Gu, Zixian Lin
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引用次数: 10

Abstract

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.
基于多目标优化的迁移学习参数自动共享
迁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。“迁移什么”是迁移学习中的一个主要研究问题。现有的迁移学习方法一般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。迁移学习模型本质上是一个特殊的多目标优化问题。为此,本文提出了一种基于多目标优化的迁移学习自动共享参数技术,并利用多群粒子群优化器解决了迁移学习的优化问题。每个任务目标由子群同时优化。利用目标任务子群中当前最优粒子来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来共同求解,这是一种归纳偏差。在多个合成数据集以及学校数据集和地雷数据集两个真实数据集上进行了实验,验证了算法的有效性。
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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来源期刊
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering 工程技术-工程:综合
CiteScore
9.90
自引率
21.50%
发文量
21
审稿时长
>12 weeks
期刊介绍: Integrated Computer-Aided Engineering (ICAE) was founded in 1993. "Based on the premise that interdisciplinary thinking and synergistic collaboration of disciplines can solve complex problems, open new frontiers, and lead to true innovations and breakthroughs, the cornerstone of industrial competitiveness and advancement of the society" as noted in the inaugural issue of the journal. The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality. Areas covered by the journal include (but are not limited to) artificial intelligence, advanced signal processing, biologically inspired computing, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, internet-based technologies, knowledge discovery and engineering, machine learning, mechatronics, mobile computing, multimedia technologies, networking, neural network computing, object-oriented systems, optimization and search, parallel processing, robotics virtual reality, and visualization techniques.
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