Research On Deep Multi-Source Domain Adaption Recognition Method Under Complex Data Conditions

Unsupervised domain adaptation refers to the process of minimizing the distributional discrepancy between a labeled source domain and an unlabeled target domain,with the objective of transferring knowledge learned from the source domain to the target domain.By doing so,it addresses the challenge of inadequate labeled data in the target domain for training deep neural networks.Currently,most unsupervised domain adaptation methods focus on a single source domain,ignoring the fact that source data can be obtained from multiple sources,and that the feature distributions between different sources and the target domain may vary in similarity,and even the classes may differ between domains.Single-source unsupervised domain adaptation methods perform poorly in practical data settings with multiple source domains.Therefore,some researchers have proposed multi-source unsupervised domain adaptation methods,whose performance largely depends on the degree of utilization of the feature information between different source domains,and the feature selection directly affects the accuracy of target domain recognition.Deliberately extracting domain-invariant features through domain alignment or domain adversarial methods,followed by mixing and weighting of outputs from various source domains at the decision level,may not fully leverage the feature information of each domain,which may cause more negative transfer and result in suboptimal or non-optimal multi-source domain adaptation In addition,effective solutions have not yet been proposed for open-set data conditions with different categories among multiple source domains and the target domain.Therefore,we explore the optimization direction of current multi-source domain adaptation methods for closed-set and open-set data conditions,constructs models to upgrade the recognition accuracy of unlabeled target domain samples,and summarizes the main research content and innovation as follows.(1)We propose the multi-source domain adaptation of dynamic domain discrepancy adjustment(DDDA).In closed-set settings,the performance of multi-source unsupervised domain adaptation methods largely depends on the relative weights of each source domain loss.However,manually adjusting these weights is difficult and complex,hindering the practical application of multi-source domain adaptation.To address this issue,we define a loss function composed of multiple source domain discrepancy terms,and dynamically adjusts the weight proportions of each source domain loss to reduce the domain discrepancies and better leverage the source domain information that is more similar to the target domain.In addition,a non-linear projection head is introduced to capture higher-quality feature information and avoid losing important features when measuring the domain discrepancies between the labeled source domains and target domain.These improvements enhance the multi-source domain adaptation process and improve the recognition accuracy of unlabeled target domain samples.(2)In real-world applications,it is difficult to maintain complete consistency between the target domain,source domain,and the categories among different source domains.In the context of open-set data settings,this paper further extends the category attributes of multiple domains,allowing for a certain proportion of private categories in each domain.Based on this data condition,a multi-source domain adaptation of multi-path information fusion adversarial network(MIFA)model is constructed.From the perspective of feature fusion between domains and within categories,the model improves the representation quality without introducing additional prior knowledge,while further preserving the feature information of each domain and corresponding category.Under the premise of obtaining high-quality representations,the private categories in the target domain not covered by the current source domain are eliminated through multi-domain adversarial training,and based on this,the combination of knowledge from multiple source domains can effectively identify target domain data.(3)We provide an explanation of the professional terminology and problem definitions involved in unsupervised domain adaptation.It also summarizes the current research status of unsupervised domain adaptation methods and outlines the development trajectory of closed-set and open-set unsupervised domain adaptation methods.In particular,it focuses on analyzing the problem formulation,solution approaches,and potential loopholes of existing methods,and conducting benchmarking experiments of these methods under different data settings.To validate the effectiveness of the proposed methods,this paper conducts experiments on several publicly available datasets in multiple domain adaptation domains and compares them with multiple classic methods to comprehensively evaluate the performance of the two constructed models.For both closed-set and open-set data settings,the two models constructed in this paper demonstrate superior performance on different datasets,outperforming existing single-source and multi-source domain adaptation methods.In addition,ablation experiments are also conducted to further analyze and demonstrate the importance of each component of the models.