# Cross-Camera Erased Feature Learning for Unsupervised Person Re-Identification
## 2 Related Work  
Supervised person re-identification. Most of existing methods are proposed to deal with supervised person re-identification, such as distance measures learning, subspaces learning [2; 3; 4], and deep learning methods [18; 19]. With the rapid development of deep learning, feature learning by deep networks has become a common practice in person re-identification. [PERSON] et al. [20] combined deep siamese network architecture with pedestrian human body feature learning for the first time and achieved higher performance. Proposed methods also improved the performance of deep person re-identification. [PERSON] et al. [21] improved the generalization of different pedestrian scenes by using Domain Guided Dropout method. Part-based methods [19; 22] achieve the most advanced performance. In part-based methods, the input feature map is divided into a fixed number of bands, and feature vectors are aggregated from these bands. BFE model [23] get better results than part-based model in supervised person re-identification. However, supervised person re-identification methods rely on a large amount of manually labeled datasets which is time-consuming and labor-intensive. It also limit the practicability and applicability of supervised methods. In this paper, BFE network is applied to unsupervised person re-identification, which strengthens the classification ability of our model.  
Unsupervised person re-identification. There are few effective methods have been used in unlabeled datasets. Features of manual annotation [5; 24; 25] are still insufficient for guaranteeing visually invariant features. The performance of manually labeled features in unsupervised pedestrian re-identification is weak. Recently. some methods achieved better results extracting features [26; 27; 28] or finding base labels [29; 30; 31] in unlabeled datasets. An unsupervised asymmetric distance metric learning method is proposed and it is based on asymmetric K-means clustering method [30; 31]. [PERSON] et al. [32] uses the clustering method for completely unsupervised person re-identification. However, the pseudo-labels of person images obtained through clustering may assign the same pseudo-labels to similar images with different identities, which improving the difficulty of classifying pedestrians. Cross-domain transfer learning methods [1; 7; 8; 10; 11] are proposed for unsupervised person re-identification, and the performance of the model on the target dataset is improved by using other labeled dataset. [PERSON] et al. [7] use GAN to reduce the gap between different datasets. [PERSON] et al. [1] propose an adaptive technology that uses synthetic data to identify person. [PERSON] et al. [33] used auxiliary datasets to generate auxiliary references for unsupervised person re-identification. However, the gap between pedestrian pictures in different datasets is really large, and the robustness of transfer learning still needs to be improved. [PERSON] et al. [34] develops the PatchNet network to learn discriminative features at patch level and shows superior performance. However, these similar patches may be extracted from different pedestrian identities, which is still difficult to distinguish potential identity information. In our model, we partially occluding pedestrian images and the remaining feature information can still belong to the same pedestrian. In addition, dataset generated in Camstyle is directly used as positive sample for calculation, so that the operation is more direct and convenient.