• 模式识别与人工智能 •

### 融合动态残差的多源域自适应算法研究

1. 中国石油大学（华东） 计算机科学与技术学院，山东 青岛 266580
• 出版日期:2022-04-01 发布日期:2022-04-01

### Research on Multi-Source Domain Adaptive Algorithm Integrating Dynamic Residuals

WANG Bin, LI Xin

1. College of Computer Science and Technology, China University of Petroleum（East China）, Qingdao, Shangdong 266580, China
• Online:2022-04-01 Published:2022-04-01

Abstract: Multi-source domain adaptation problem usually refers to a scene with multiple source domains and a single target domain. The common approach is to align the distribution of source domain and target domain according to the domain label, and map the distribution to the common hidden space by reducing the distance between domains to predict the data classification of unknown target domain. The source data set usually needs domain label, and the parameters of the model are fixed after the training stage, which is difficult to fit the distribution of unknown target domain. The multi-source domain adaptive algorithm based on dynamic residual block generates neural network parameters not from the perspective of domain, but from the feature mapping of data itself. Without domain label, the multi-source domain adaptive problem is transformed into a single source domain problem. Moreover, the dynamic residual block can change the network parameters across stages according to the characteristics of the input data, better let the network parameters fit the untrained target domain data distribution, simplify the complexity of multi-source domain adaptive model design, and reduce the workload of data preparation. The experimental results show that the accuracy is improved by 8.1% compared with the static model, and the running time and space of the model are saved.