Research On Deep Domain Adaptation Method Based On Cross-domain Knowledge Diversity Minin

The main task of domain adaptation is to deal with decision-making problems for similar tasks across different data distributions.The rise of deep learning has brought more attention to domain adaptation research,such as image classification and object detection tasks.Due to the complex and diverse application scenarios of domain adaptation,most methods fail to effectively explore explicit and implicit information and rarely consider the dynamic relationship between domain alignment and domain discrimination ability.Existing methods also fail to take into account the preservation and utilization of cross-domain common knowledge for difficult and simple samples at the class level.To address the above issues,we mainly conduct in-depth exploration and utilization of the diversity of cross-domain knowledge to achieve better domain adaptation performance.In summary,our main works are as follows:(1)Unsupervised domain adaptation via bidirectional generation and middle domains alignment.To tackle the problem of insufficient feature information feedback in current adversarial domain adaptation methods.We first review existing adversarial-based unsupervised domain adaptation methods,and then we propose the middle domain transition method(BGMA)from the perspective of intermediate domain transition,which aligns middle domains to effectively reduce differences in data distribution and uses data generation and supervisory constraints to minimize middle domain gaps.Consequently,it enhances the shared information between domains and reduces the difficulty of transferring domain knowledge.(2)Unsupervised domain adaptation through dynamically aligning both the feature and label spaces.To address the issue of imbalanced domain alignment and discrimination capability in current existing adversarial domain adaptation methods,we propose the DAFL method with a focus on dynamic balance.By measuring the distance of the class-level distribution between the source and target domains,we adaptively learns weights dynamically between domain adaptation and domain discrimination for improving the performance of target domain classification tasks in DAFL.Specifically,we first perform alignment of class-level in the feature space and maximize label space information to reduce the cross-domain distribution differences.Additionally,dynamically adjusting the balance factor between domain alignment and discrimination helps to prevent the model from getting stuck in suboptimal solutions and helps with labeling target domain samples.(3)Active domain adaptation based on updated class consensus dictionary.We propose the UCCDA method to address the problem of insufficient information mining of both simple and tough samples in active domain adaptation methods.Firstly,We utilize prior knowledge from the source domain to initialize a class consensus dictionary.Then we actively select tough samples for manual labeling through active learning,and select simple confident samples to assign pseudo-labels by self-training manner.In addition,we can effectively reduce the cross-domain distribution difference between the source and target domains by simultaneously updating tough and simple samples in the class consensus dictionary.Finally,UCCDA can also be applied to source-free domain adaptation problems under privacy protection mechanisms.