基于可调节判别器的领域适应

兰州理工大学学报 ›› 2022, Vol. 48 ›› Issue (1): 85-90.

• 自动化技术与计算机技术 • 上一篇下一篇

基于可调节判别器的领域适应

赵小强^*1,2,3, 蒋红梅¹

1.兰州理工大学电气工程与信息工程学院, 甘肃兰州 730050;
2.兰州理工大学甘肃省工业过程先进控制重点实验室, 甘肃兰州 730050;
3.兰州理工大学国家级电气与控制工程实验教学中心, 甘肃兰州 730050

收稿日期:2020-07-27 出版日期:2022-02-28 发布日期:2022-03-09
通讯作者: 赵小强 (1969-),男,陕西岐山人,博士,教授,博导.Email:xqzhao@lut.edu.cn
基金资助:
国家自然科学基金(62163023),国家重点研发计划项目(2020YFB1713600),甘肃省教育厅产业支撑计划项目(2021CYZC-02)

Domain adaptation based on adjustable discriminator

ZHAO Xiao-qiang^1,2,3, JIANG Hong-mei¹

1. College of Electrical Engineering and Information Engineering, Lanzhou Univ. of Tech., Lanzhou 730050, China;
2. Key Laboratory of Gansu Advanced Control for Industrial Processes,Lanzhou Univ. of Tech., Lanzhou 730050, China;
3. National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou Univ. of Tech., Lanzhou 730050, China

Received:2020-07-27 Online:2022-02-28 Published:2022-03-09

摘要/Abstract

摘要： 当前基于对抗学习的领域适应(DA)对目标样本的适应性较差,导致目标域的预测精确度较低,为此提出基于可调节判别器的领域适应(A-DADA)算法.首先,利用两个判别器分类概率的距离作为权重应用到目标域对抗训练损失函数中,旨在减少已对齐目标样本对抗训练的次数同时增加未对齐目标样本的对抗训练次数;其次,将平方熵损失函数作为最小熵损失函数,提高类平衡性;最后,使用Office-31数据集将该算法与JAN等算法进行对比实验,实验结果表明,与JAN算法相比,该算法的平均精度值提高了2.5%.

关键词: 领域适应, 可调节判别器, 对抗训练, 平方熵损失

Abstract: The current domain adaptation (DA) based on adversarial learning has poor adaptability to target samples, lower prediction accuracy of the target domain is caused. For this reason, this paper proposes a domain adaptation (A-DADA) algorithm based on an adjustable discriminator. First, the distance between the classification probabilities of the two discriminators is used as the weight to apply to the target domain confrontation training loss function, aiming to reduce the number of confrontation training times for aligned target samples and increase the number of confrontation training times for unaligned target samples. Secondly, square entropy loss function is used as the minimum entropy loss function to improve the class balance. Finally, the Office-31 datasetis used to compare the algorithm with JAN and other algorithms, the experimental results show that compared with the JAN algorithm, the average accuracy of the algorithm is increased by 2.5%.

Key words: domain adaptation, adjustable discriminator, adversarial training, square entropy loss

中图分类号:

TP391.41

赵小强, 蒋红梅. 基于可调节判别器的领域适应[J]. 兰州理工大学学报, 2022, 48(1): 85-90.

ZHAO Xiao-qiang, JIANG Hong-mei. Domain adaptation based on adjustable discriminator[J]. Journal of Lanzhou University of Technology, 2022, 48(1): 85-90.

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基于可调节判别器的领域适应

Domain adaptation based on adjustable discriminator

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