## 1 Introduction  
With the rapid development of Deep Learning, person re-Identification based on deep convolutional neural networks has achieved remarkable success in the last few years. The goal of Re-ID is to retrieve images of person-of-interest. Most researchers of the Re-ID community focus on modifying some pre-trained convolutional neural network backbones such as ResNet [1] and its variants in order to improve the performance of Re-ID. The Re-ID is a challenging task result from the presence of different viewpoints, varying low-image resolutions, illumination changes, unconstrained poses, occlusions, heterogeneous modalities, etc. Ref. [2]. Prevailing CNN models for Re-ID has greatly improved the rank-1 accuracy on the Market-1501 [3] and other benchmarks due to the excellent representation capability of features. However, manually designing an efficient convolutional neural network backbone usually needs a group of human experts and costs a lot of time. Another problem is that these handcraft models were oriented, designed for image classification tasks, and the Re-ID task is mainly for retrieval, so there is still a lot of room for improvement in Re-ID tasks. As an important branch of Automated Machine Learning (AutoML), Neural Architecture Search (NAS) expects to design an efficient architecture for Re-ID tasks automatically, rather than designing the convolutional neural network backbone for Re-ID manually. With the growth of NAS, the time for a well-performing architecture of a particular task can be reduced from months to hours. Re-ID is essentially a retrieval task, but current NAS algorithms are merely designed for classification. Manually designing a CNN network that is suitable for Re-ID task is not only time-consuming but also heavily relies on professional knowledge. In light of this, we propose to automatically search for a CNN architecture that is specifically suitable for the Re-ID task. Due to the fact that current NAS algorithms are only for classification purposes, while Re-ID is essentially a retrieval task. A retrieval based search algorithm over a specifically designed Re-ID search space is necessary since Re-ID is different from classification. Auto-ReID [4] took the first step to automate the Re-ID model design. They have removed some redundant operations and designed a new operation called a part-aware module that can retain body structural information of humans to improve the performance of the searched Re-ID model. Another crucial contribution is that they introduced retrieval loss in order to fit into the Re-ID task. Different from Auto-ReID, we proposed a novel deep-based Re-ID algorithm called KDAS-ReID that can automate the design of Re-ID architectures based on knowledge distillation. Figure 1 showed that knowledge distillation aims at transferring knowledge from a pre-trained teacher model to a student model. [PERSON] et al. [5] defined knowledge distillation as training a smaller and faster student model to approach the teacher's outputs after softmax. Motivated by this, we considered an approach to apply the NAS algorithm in Re-ID tasks and make the final architecture that can be easily deployed through taking a pre-trained network with high performance on Re-ID as the teacher model during the search stage. Our contributions can be summarized as follows:  
* Person re-identification is essentially a retrieval problem that aims at retrieving images of persons-of-interest. Previous Re-ID convolutional neural networks are usually built upon backbones for image classification, such as ResNet [1], Inception [6], and VGG [7]. In this paper, the NAS algorithm is recommended based on knowledge distillation that aims at designing a convolutional neural network backbone that performed well in the Re-ID task. More concretely, we first trained a teacher model on Market-1501 which built upon ResNet backbones and pre-trained on the ImageNet.