## 5 Conclusions  
In this paper, we propose a novel algorithm that automated neural architecture search for the Re-ID tasks based on Knowledge Distillation, and we name our method as KDAS-ReID. We have made progress based on Auto-ReID via transferring the knowledge from the teacher model to the student model. Furthermore, we introduced distillation loss with a dynamic temperature in order to break the limit of the teacher model. In our experiments, the KDAS-ReID outperforms other state-of-the-art Re-ID models on Market-1501, CUHK03, and MSMT17. In addition, the KDAS-ReID also outperforms the teacher model which was built upon the ResNet-50 backbone and using pre-training on the ImageNet. In the future, we consider the other ways of knowledge distillation such as employing the internal representation of the teacher to guide the searching and training of the student.  
**Author Contributions:** Conceptualization, K.Y.; Formal analysis, K.Y.; Project administration, Z.L.; Software, K.J.; Validation, S.C. and Z.L.; Writing--original draft, K.Y. and K.J.; Writing--review & editing, S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61572306 and Grant 61502294, in part by the National Key Research and Development Program of China under Grant 2017 YFB0701600, and in part by the Shanghai Engineering Research Center of Intelligent Computing System under Grant 19 DZ2252600.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** The authors thank the High-Performance Computing Center of Shanghai University for providing computing resources.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline GoogleNet [6] & 47.6 & 65.0 & 71.8 & 23.0 \\ PDC [12] & 58.0 & 73.6 & 79.4 & 29.7 \\ GLAD [44] & 61.4 & 76.8 & 81.6 & 34.0 \\ PCB [11] & 68.2 & 81.2 & 85.5 & 40.4 \\ \hline Auto-ReID [4] & 78.2 & 88.2 & 91.1 & 52.5 \\ \hline
**Teacher Model** & 77.1 & 86.6 & 90.1 & 51.2 \\
**KDAS-ReID** & **78.4** & **88.3** & **91.1** & **53.2** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of accuracy and mAP with the state-of-art reID models on MSMT17. We have boldfaced the best results in the table.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Labeled**} & \multicolumn{2}{c}{**Detected**} \\ \cline{2-5}  & **Rank-1** & **mAP** & **Rank-1** & **mAP** \\ \hline PAN [30] & 36.9 & 35.0 & 36.3 & 34.0 \\ SVDNet [41] & 40.9 & 37.8 & 41.5 & 37.3 \\ HA-CNN [42] & 44.4 & 41.0 & 41.7 & 38.6 \\ AOS [35] & - & - & 47.7 & 43.3 \\ MLFN [36] & 54.7 & 49.2 & 52.8 & 47.8 \\ PCB [11] & - & - & 63.7 & 57.5 \\ Mancs [38] & 69.0 & 63.9 & 65.5 & 60.5 \\ DG-Net [43] & - & - & 65.6 & 61.1 \\ \hline Auto-ReID [4] & 77.9 & 73.0 & 73.3 & 69.3 \\ \hline
**Teacher Model** & 76.3 & 71.5 & 71.9 & 68.0 \\
**KDAS-ReID** & **78.0** & **73.2** & **73.4** & **70.0** \\ \hline \hline \end{tabular}
\end{table}
Table 3: We use the new evaluation protocol in [30] to evaluate on the data set CUHK03. We compare with the most advanced Re-ID model in terms of Rank-1 accuracy and mAP. We have boldfaced the best results in the table.