# Unsupervised Domain Adaptive Person Re-Identification via Intermediate Domains
## 4 Experimental Results and Discussion
### Further Analysis
#### 4.5.2 Number of Intermediate Domains  
We set the domainness factor to \(\lambda=0.2,0.5,0.8\) and obtained three intermediate domains in this study. We also chose two and four intermediate domains for the experiments. The results are listed in Table 5.  
The experimental results demonstrated that three intermediate domains achieve the best performance. If we only select two intermediate domains, the domain gap among  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Market1501\(\rightarrow\)MSMT17**} & \multicolumn{5}{c}{**DukeMTMC-reID\(\rightarrow\)MSMT17**} \\ \cline{2-10}  & & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** \\ \hline ECN [32] & CVPR’19 & 8.5 & 25.3 & 36.3 & 42.1 & 10.2 & 30.2 & 41.5 & 46.8 \\ SSG [34] & ICCV’19 & 13.2 & 31.6 & - & 49.6 & 13.3 & 32.3 & - & 51.2 \\ MMCL [35] & CVPR’20 & 15.1 & 40.8 & 51.8 & 56.7 & 16.2 & 43.6 & 54.3 & 58.9 \\ ECN++ [38] & TPAMI’20 & 15.2 & 40.4 & 53.1 & 58.7 & 16.0 & 42.5 & 55.9 & 61.5 \\ NRMT [17] & ECCV’20 & 19.8 & 43.7 & 56.5 & 62.2 & 20.6 & 45.2 & 57.8 & 63.3 \\ MMT [5] & ICLR’20 & 22.9 & 49.2 & 63.1 & 68.8 & 23.3 & 50.1 & 63.9 & 68.8 \\ SpCL [16] & NeurIPS’20 & 25.4 & 51.6 & 64.3 & 69.7 & 26.5 & 53.1 & 65.8 & 70.5 \\ UNRN [6] & AAAI’21 & 25.3 & 52.4 & 64.7 & 69.7 & 26.2 & 54.9 & 67.3 & 70.6 \\ \hline Baseline & & 25.1 & 51.1 & 62.4 & 67.6 & 27.6 & 55.4 & 68.3 & 72.7 \\ Ours & & **27.6** & **55.5** & **66.8** & **71.7** & **29.5** & **58.3** & **69.7** & **74.3** \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Numbers of**} & \multicolumn{2}{c}{**Duke\(\rightarrow\)Market**} & \multicolumn{2}{c}{**Market\(\rightarrow\)Duke**} \\ \cline{2-5}  & **mAP** & **Rank-1** & **mAP** & **Rank-1** \\ \hline
2 & 78.8 & 92.1 & 67.6 & 81.1 \\
3 & **80.2** & **92.4** & **69.1** & **82.3** \\
4 & 79.7 & 92.0 & 67.8 & 81.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The effect of numbers of intermediate domains on the performance of the model.  
these domains is relatively great. We obtained additional wrong labels during the process of clustering.  
However, the number of intermediate domains is not as large as possible. Style differences among different datasets are relatively small compared with style transfer tasks for the ReID dataset. Additional domainness factors will lead to smaller differences among intermediate domains. On one hand, it is difficult for the GAN model to generate such images. On the other hand, fine-tuning on such datasets will lead to overfitting on intermediate domains. Hence, the model achieves a poor performance on the target domain.  
The combination of different intermediate domains will also exert a serious influence on the experimental results. For example, the results of m2d_0.2 and m2d_0.8 are different from those of m2d_0.5 and m2d_0.8, although both findings are worse than when three intermediate domains are used.  
Furthermore, the quality of intermediate domain images plays an important role in improving the performance. We further used cycleGAN to generate intermediate datasets. The performance of the UDA baseline is presented in Table 6. The experimental result of using cycleGAN to generate the intermediate domains is approximately 2% lower than that of SPGAN because SPGAN is an improvement that focuses on the ReID problem based on cycleGAN. Hence, the quality of the image generated by SPGAN is better than that of cycleGAN. The experimental results can be improved by using an enhanced GAN model.  
Intermediate domains demonstrate a high application potential in many computer vision tasks, and obtaining high-quality intermediate domain images can be the focus of a future investigation.