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It takes two: Dual Branch Augmentation Module for domain generalization

文献类型：期刊论文

作者Li, Jingwei2,3; Li, Yuan2,3; Tan, Jie3; Liu, Chengbao1,2,3
刊名NEURAL NETWORKS
出版日期2024-04-01
卷号172页码:12
ISSN号0893-6080
关键词Domain generalization Fourier transform Uncertainty calibration Test-time adaptation
DOI10.1016/j.neunet.2023.106094
通讯作者Liu, Chengbao(liuchengbao2016@163.com)
英文摘要Although great success has been achieved in various computer vision tasks, deep neural networks (DNNs) suffer dramatic performance degradation when evaluated on out-of-distribution data. Domain generalization (DG) is proposed to handle this problem by learning domain-agnostic information from multiple source domains to generalize well on unseen target domains. Several methods resort to Fourier transform due to its simplicity and efficiency. They argue that amplitude spectra imply domain-specific information, which should be suppressed, while phase counterparts imply domain-agnostic information, which should be preserved. However, these methods only suppress the domain-specific information in source domains and neglect the relationship with target domains, leading to the persistence of the domain gap. Besides, these methods preserve domain-agnostic information by keeping phase components unchanged, causing them to be underutilized. In this paper, we propose Dual Branch Augmentation Module (DBAM) by leveraging Fourier transform and taking advantage of both amplitude and phase spectra. For the amplitude branch, we propose Inner-domain Amplitude Distribution Rectification (IADR) and Cross-domain Amplitude Dirichlet Mixup (CADM) to stabilize the training process and explore more feature space. In addition, we propose Test-time Amplitude Prototype Calibration (TAPC) to construct the connection between source and target domains during evaluation to further mitigate the domain gap. For the phase branch, we propose Random Symmetric Phase Perturbation (RSPP) to enhance the robustness for recognizing domain-agnostic information. With the combined contributions of the two branches, DBAM significantly surpasses other state-of-the-art (SOTA) methods. Extensive experiments on four benchmarks and further analysis demonstrate the effectiveness of DBAM.
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:001157966200001
源URL[http://ir.ia.ac.cn/handle/173211/55643]  
专题中科院工业视觉智能装备工程实验室
通讯作者Liu, Chengbao
作者单位1.Chinese Acad Sci, Inst Automat, 95 East Zhongguancun Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Jingwei,Li, Yuan,Tan, Jie,et al. It takes two: Dual Branch Augmentation Module for domain generalization[J]. NEURAL NETWORKS,2024,172:12.
APA Li, Jingwei,Li, Yuan,Tan, Jie,&Liu, Chengbao.(2024).It takes two: Dual Branch Augmentation Module for domain generalization.NEURAL NETWORKS,172,12.
MLA Li, Jingwei,et al."It takes two: Dual Branch Augmentation Module for domain generalization".NEURAL NETWORKS 172(2024):12.

入库方式： OAI收割

来源：自动化研究所

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