# Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer
###### Abstract  
The unsupervised domain-adaptive vehicle re-identification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain; however, there are knowledge differences between the target domain and the source domain. To mitigate domain discrepancies, existing unsupervised domain-adaptive re-identification methods typically require access to source domain data to assist in retraining the target domain model. However, for security reasons, such as data privacy, data exchange between different domains is often infeasible in many scenarios. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a source-free domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the \"source-like samples\". Then, it can effectively reduce the model knowledge difference and improve the model's generalization performance. In the experiment, two mainstream public datasets in this field, VeRi776 and VehicleID, are tested experimentally, and the obtained rank-k (the cumulative matching features) and mAP (the mean Average Precision) indicators are both improved, which are suitable for object re-identification tasks when data between domains cannot be interoperated. vehicle re-identification; unsupervised domain adaptation; source-free knowledge transfer; pseudo label; joint training +
Footnote †: journal:  
[PERSON] et al. [14] proposed a viewpoint-aware clustering algorithm. It leverages a pre-trained vehicle orientation predictor to predict the orientations of vehicles and assign directional pseudo labels. First, it clusters vehicles with the same perspective. Subsequently, it clusters vehicles with different perspectives, thereby enhancing the performance of the vehicle re-identification model. [PERSON] et al. [15] proposed a progressive learning method named PLM for vehicle re-identification in unknown domains. The method utilizes domain adaptation and a multi-scale attention network to smooth domain bias, trains a relD model, and introduces a weighted label smoothing loss to improve performance. The above methods preserve the identity information from a well-labeled source domain while learning the style of the unlabeled target domain, but usually face the problem that when learning an adaptive model on the target domain, it is unavoidable to visit the source domain to generate new samples for subsequent fine-tuning. However, due to issues related to data ownership and privacy concerns, such as the fact that vehicle data between different cities are typically not shared, in many cases data exchange between domains is infeasible. Consequently, the target domain model cannot directly access source domain data, greatly impacting the model's adaptability performance. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer; that is, a target domain sample is given, the migration image of the sample is obtained through the generator, and the two images are, respectively, provided to the target model and the source model. The, the difference between the image pair compensates for the knowledge difference between the domain models, so that the output of the two domain models is similar, and then the generator is trained by constraining the output similarity, and the target domain data can be transformed to have the style of the source domain. Therefore, these \"source-like samples\" can replace the role played by the source domain data in the model adaptation of the target domain, and since the generated sample content is provided by the target domain, it is has more affinity in the process of model adaptation, which helps to solve the problem that the target domain cannot access the data of the source domain. The method can be divided into two stages:  
1. In the first stage, we construct a source-free knowledge transfer module.