Domain generalization method of phase-frequency fusion from independent perspective

doi:10.11772/j.issn.1001-9081.2023050623

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (4): 1002-1009.DOI: 10.11772/j.issn.1001-9081.2023050623

• The 9th National Conference on Intelligent Information Processing（NCIIP 2023） • Previous Articles Next Articles

Domain generalization method of phase-frequency fusion from independent perspective

Bin XIAO¹^,², Mo YANG¹^,², Min WANG³(), Guangyuan QIN¹, Huan LI⁴

^1.School of Computer Science and Software Engineering，Southwest Petroleum University，Chengdu Sichuan 610500，China
^2.Big Data and Knowledge Engineering Research Center，Southwest Petroleum University，Chengdu Sichuan 610500，China
^3.School of Electrical Engineering and Information，Southwest Petroleum University，Chengdu Sichuan 610500，China
^4.Communication and Information Technology Center，Southwest Oil & Gas Field Company，Chengdu Sichuan 610501，China

Received:2023-05-22 Revised:2023-06-14 Accepted:2023-06-15 Online:2023-08-01 Published:2024-04-10
Contact: Min WANG
About author:XIAO Bin， born in 1978， M. S.， professor. His research interests include software engineering， enterprise informatization.
YANG Mo， born in 1999， M. S. candidate. His research interests include domain generalization， causal learning.
WANG Min， born in 1980， M. S.， professor. Her research interests include active learning， signal and information processing.
QIN Guangyuan， born in 1978， M. S.， lecturer. His research interests include software engineering， digital oilfield.
LI Huan， born in 1993， assistant researcher. Her research interests include digital oilfield， software engineering.
Supported by:
National Natural Science Foundation of China(62006200);Sichuan Science and Technology Program(2022YFG0179);Project of State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering(PLC20211104)

独立性视角下的相频融合领域泛化方法

肖斌¹^,², 杨模¹^,², 汪敏³(), 秦光源¹, 李欢⁴

^1.西南石油大学计算机与软件学院，成都 610500
^2.西南石油大学大数据与知识工程研究中心，成都 610500
^3.西南石油大学电气信息学院，成都 610500
^4.西南油气田公司通信与信息技术中心，成都 610501

通讯作者: 汪敏
作者简介:肖斌（1978—），男，重庆人，教授，硕士，CCF会员，主要研究方向：软件工程、企业信息化
杨模（1999—），男，四川渠县人，硕士研究生，主要研究方向：领域泛化、因果学习
汪敏（1980—），女，湖南邵阳人，教授，硕士，CCF会员，主要研究方向：主动学习、信号和信息处理 wangmin80616@163.com
秦光源（1978—），男，四川蓬溪人，讲师，硕士，主要研究方向：软件工程、数字油田
李欢（1993—），女，四川成都人，助理研究员，主要研究方向：数字油田、软件工程。
基金资助:
国家自然科学基金资助项目(62006200);四川省科技计划项目(2022YFG0179);油气藏地质及开发工程国家重点实验室（成都理工大学）项目(PLC20211104)

Abstract

Abstract:

The existing Domain Generalization （DG） methods process the domain features poorly and have weak generalization ability， thus a method based on the feature independence of the frequency domain was proposed to solve the domain generalization problem. Firstly， a frequency domain decomposition algorithm was designed to obtain domain-independent features from phase information by the Fast Fourier Transform （FFT） of depth features of the image， improving the recognition ability of domain-independent features. Secondly， from the independence perspective， the correlation of attributes in frequency domain features was further eliminated by weighting the features of samples， and the most effective domain-independent features were extracted to solve the poor generalization problem caused by correlation between sample features. Finally， the amplitude fusion strategy was proposed to narrow the distance between the source domain and the target domain， so as to further improve the generalization ability of the model to the unknown domain. Experimental results on popular image domain generalization datasets PACS and VLCS show that the average accuracy of the proposed method is 0.44， 0.59 percentage points higher than that of StableNet， and the proposed method achieves excellent performance on all datasets.

Key words: Domain Generalization (DG), image classification, deep neural network, independent learning, phase-frequency fusion

摘要：

针对现有的领域泛化（DG）方法对领域特征处理粗糙和泛化能力弱的问题，提出一种基于频域特征独立性这一独特视角解决领域泛化问题的方法。首先，设计频域分解算法，将图像的深度特征快速傅里叶变换（FFT）后，再从相位信息中获得领域无关特征，以提高模型对领域无关特征的识别能力；其次，基于独立性视角，通过对样本的特征赋权，进一步消除频域特征中各属性的相关性，提取最有效领域无关特征，解决样本特征之间相关性带来的泛化能力差的问题；最后，提出幅度融合策略，拉近源域和目标域的距离，进一步提升模型对未知领域的泛化能力。在流行的图像领域泛化的数据集PACS和VLCS上的实验结果表明，所提方法的准确率均值比StableNet分别高0.44、0.59个百分点，且在各个数据集上均取得了优秀的性能。

关键词: 领域泛化, 图像分类, 深度神经网络, 独立性学习, 相频融合

CLC Number:

TP183

Bin XIAO, Mo YANG, Min WANG, Guangyuan QIN, Huan LI. Domain generalization method of phase-frequency fusion from independent perspective[J]. Journal of Computer Applications, 2024, 44(4): 1002-1009.

肖斌, 杨模, 汪敏, 秦光源, 李欢. 独立性视角下的相频融合领域泛化方法[J]. 《计算机应用》唯一官方网站, 2024, 44(4): 1002-1009.

Figures/Tables 9

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数据集	类别	领域数	图像数
PACS	7	4	9 991
VLCS	5	4	12 237
Office-Home	65	4	15 500

数据集	类别	领域数	图像数
PACS	7	4	9 991
VLCS	5	4	12 237
Office-Home	65	4	15 500

方法	PACS				均值
方法	Art	Cartoon	Photo	Sketch	均值
ResNet-18	76.61	73.60	93.31	76.08	79.90
MLDG	79.50	77.30	94.30	71.50	80.65
MMLD	81.28	77.16	96.09	72.29	81.71
FAR	79.30	77.70	95.30	74.70	81.75
MixStyle	84.10	78.80	96.10	75.90	83.73
StableNet	81.74	79.91	96.53	80.50	84.69
StableNet*	83.45	79.60	94.97	78.92	84.24
RSC	83.43	80.31	95.99	80.85	85.15
RSC*	80.55	78.60	94.43	76.02	82.40
MASF	80.29	77.17	94.99	71.79	81.06
FACT	85.37	78.38	95.15	79.15	84.51
本文方法	84.47	81.27	94.61	80.17	85.13

方法	PACS				均值
方法	Art	Cartoon	Photo	Sketch	均值
ResNet-18	76.61	73.60	93.31	76.08	79.90
MLDG	79.50	77.30	94.30	71.50	80.65
MMLD	81.28	77.16	96.09	72.29	81.71
FAR	79.30	77.70	95.30	74.70	81.75
MixStyle	84.10	78.80	96.10	75.90	83.73
StableNet	81.74	79.91	96.53	80.50	84.69
StableNet*	83.45	79.60	94.97	78.92	84.24
RSC	83.43	80.31	95.99	80.85	85.15
RSC*	80.55	78.60	94.43	76.02	82.40
MASF	80.29	77.17	94.99	71.79	81.06
FACT	85.37	78.38	95.15	79.15	84.51
本文方法	84.47	81.27	94.61	80.17	85.13

算法	Office-Home				均值
算法	Art	Clipart	Product	Real-World	均值
MMD-AAE	56.50	47.30	72.10	74.80	62.68
DADG	55.57	48.71	70.90	73.70	62.22
JiGen	53.04	47.51	71.47	72.79	61.20
RSC	58.42	47.90	71.63	74.54	63.12
SagNet	60.20	45.30	70.40	73.30	62.30
本文方法	57.10	52.42	72.25	73.90	63.92

Domain generalization method of phase-frequency fusion from independent perspective

独立性视角下的相频融合领域泛化方法

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Figures/Tables 9

References 40

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Metrics

算法	VLCS				均值
算法	Caltech	LabelMe	VOC	Sun	均值
JiGen	96.17	62.06	70.93	71.40	75.14
M-ADA	74.33	48.38	45.13	33.82	50.42
MMLD	97.01	62.20	73.01	72.49	76.18
RSC	96.21	62.51	73.81	72.10	76.16
StableNet	96.67	65.36	73.59	74.97	77.65
本文方法	96.75	67.17	74.61	74.41	78.24

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幅度融合	特征独立性策略	Art	Cartoo	Photo	Sketch	均值
—	—	76.61	73.60	93.31	76.08	79.90
√	—	83.31	81.23	95.63	77.45	84.41
—	√	83.44	79.60	94.97	78.92	84.32
√	√	84.47	81.27	94.61	80.17	85.13

幅度融合	特征独立性策略	Art	Cartoo	Photo	Sketch	均值
—	—	76.61	73.60	93.31	76.08	79.90
√	—	83.31	81.23	95.63	77.45	84.41
—	√	83.44	79.60	94.97	78.92	84.32
√	√	84.47	81.27	94.61	80.17	85.13