## 4 Experiment
### Comparison with Existing Technology  
Strongly supervised learning requires training data with complete and accurate labeling information; whereas unsupervised learning does not rely on any labeling information, and weakly supervised learning falls somewhere in between. By using partially labeled or incompletely labeled data to train the model, the reliance on a large amount of fully labeled data can be alleviated to a certain extent, improving the flexibility and usefulness of learning. Since the existing strongly supervised pedestrian re-identification methods cannot be directly applied to the weakly supervised environment in this paper, the method proposed in this paper is compared with unsupervised and weakly supervised pedestrian re-identification methods to confirm the effectiveness of this paper's method when compared with the strongly supervised method. Table 5 compares the experimental results of the method proposed in this paper with the strongly supervised, unsupervised, and weakly supervised pedestrian re-identification models. For strongly supervised learning, PCB, PCB+RPP [34], MGN, SVDNet [52], MSMG-Net [53], and MSAN [54] are used in this paper. For unsupervised learning, CAMEI [55], PAUL, OIMI [56], DAL [57], DBC [58], and UTAL [59] are used. For weakly supervised learning, a total of 16 methods from CV-MML, WSDDN [60], HSLR [61], and SSLR [61] are used to carry out comparative experiments, and the CAMEL method is a traditional two-phase framework, which firstly needs to extract the image features, and then learns the asymmetric representation. In the optimal unsupervised DBC, PAUL in Table 5, there is no relevant experiment on the CUHK03 dataset, this paper only compares the performance of the Market-1501 and DuKeMTMC datasets. The results \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID/Wrap** & \multicolumn{2}{c}{**CUHK03**} & \multicolumn{2}{c}{**PRID2011**} \\ \hline  & mAP & Rank-1 & mAP & Rank-1 \\
1 (Full supervision) & 68.1 & 71.5 & 56.3 & 68 \\
2 & 61.4 & 68 & 52.6 & 63 \\
3 & 55.7 & 59.4 & 48.2 & 60.4 \\
10 & 46.5 & 48.2 & 39.9 & 54.7 \\ Stochastic Averaging (5) & 62.3 & 69.6 & 53 & 65.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data diversity analysis comparison with state-of-the-art methods. in Table 5 show that the weakly supervised pedestrian re-identification method proposed in this paper obtains a significant performance improvement over the unsupervised Re-ID method, which is considerably better than the best-performing unsupervised models, DBC and PAUL. The map of the proposed method in this paper for the DuKeMTMC dataset is 59.6% lower than that of the weakly supervised learning approach, WSDDN, which is 60.7%. It is higher than that of the better-performing CV-MIML for the DuKeMTMC dataset at 59.3%, compared with other weakly supervised performances using the same dataset. This paper's method greatly improves performance and is effective when compared with other weakly supervised methods, and this paper also conducts experiments on the CUHK03 dataset, which performs well. To further prove the effectiveness of the proposed method in this paper, it is compared with the strongly supervised methods PCB, PCB + RPP, MGN, SVDNet, MSMG-Net, and MSAN. The results are shown in Table 5. The model proposed in this paper still has some gaps compared to the strongly supervised model. Given that the method in this paper saves annotation work, reduces cost, and is easy to deploy, the weakly supervised Re-ID provides a good balance between annotation and accuracy. These results validate the effectiveness and feasibility of the method proposed in this paper. To visualize the performance of the proposed method in this paper, the CMC curves of some of the compared methods are shown for the Market1501 dataset, from which it can be seen that the weakly supervised multi-feature fusion model based on weakly supervised Re-ID outperforms the purely unsupervised algorithm in the Market1501 dataset and is not too far away from the performance of the strongly supervised algorithm.