双向协同的去中心化多源域自适应

魏义康; 韩亚洪

doi:10.1631/FITEE.2200284

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双向协同的去中心化多源域自适应

常规文章 | Updated：2022-12-16

- 双向协同的去中心化多源域自适应
- Dual collaboration for decentralized multi-source domain adaptation
- 信息与电子工程前沿（英文） 2022年23卷第12期页码：1780-1794
- Affiliations：
  
  1.College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
  2.Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin 300350, China
- Author bio：
  
  E-mail: yikang@tju.edu.cn;
  ‡Corresponding author
- Funds：
  
  National Nature Science Foundation of China(61876130;61932009);Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study
- DOI：10.1631/FITEE.2200284
  中图分类号： TP181
- 收稿：2022-06-29，
  
  录用：2022-09-22，
  
  纸质出版：2022-12-0
- Accepted：
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魏义康, 韩亚洪. 双向协同的去中心化多源域自适应[J]. 信息与电子工程前沿（英文）, 2022,23(12):1780-1794.

Yikang WEI, Yahong HAN. Dual collaboration for decentralized multi-source domain adaptation[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1780-1794.
魏义康, 韩亚洪. 双向协同的去中心化多源域自适应[J]. 信息与电子工程前沿（英文）, 2022,23(12):1780-1794. DOI： 10.1631/FITEE.2200284.

Yikang WEI, Yahong HAN. Dual collaboration for decentralized multi-source domain adaptation[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1780-1794. DOI： 10.1631/FITEE.2200284.

摘要

去中心化多源域自适应是指在数据去中心化场景下执行无监督多源域自适应。数据去中心化的挑战是源域与目标域在训练中缺乏跨域协同。对于无标签的目标域，目标域模型需要在源域模型的协助下迁移监督知识，而域差距会导致源域模型的适应性能有限。对于有标签的源域，源域模型在数据去中心化场景下倾向于过拟合本地数据，从而导致负迁移问题。对于以上挑战，提出双向协同的去中心化多源域自适应方法，通过其它域模型的协助进行局部源域模型与局部目标域模型的协同训练与聚合。对于目标域，我们在源域模型的协助下蒸馏监督知识，同时完全利用无标签目标域的数据来缓解域偏移问题。对于源域，我们在目标域模型的协助下正则化源域模型来避免负迁移问题。以上过程在去中心化的源域和目标域之间形成一种双向协同，以便在数据去中心化场景下提升域自适应性能。在标准多源域自适应数据集上的实验表明，我们的方法以较大优势优于现有的多源域自适应方法。

Abstract

The goal of decentralized multi-source domain adaptation is to conduct unsupervised multi-source domain adaptation in a data decentralization scenario. The challenge of data decentralization is that the source domains and target domain lack cross-domain collaboration during training. On the unlabeled target domain

the target model needs to transfer supervision knowledge with the collaboration of source models

while the domain gap will lead to limited adaptation performance from source models. On the labeled source domain

the source model tends to overfit its domain data in the data decentralization scenario

which leads to the negative transfer problem. For these challenges

we propose dual collaboration for decentralized multi-source domain adaptation by training and aggregating the local source models and local target model in collaboration with each other. On the target domain

we train the local target model by distilling supervision knowledge and fully using the unlabeled target domain data to alleviate the domain shift problem with the collaboration of local source models. On the source domain

we regularize the local source models in collaboration with the local target model to overcome the negative transfer problem. This forms a dual collaboration between the decentralized source domains and target domain

which improves the domain adaptation performance under the data decentralization scenario. Extensive experiments indicate that our method outperforms the state-of-the-art methods by a large margin on standard multi-source domain adaptation datasets.

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