Angular regularization for unsupervised domain adaption on person re-identification.

State-of-the-art Re-ID methods based on unsupervised domain adaption transferred the domain knowledge by pre-training model on labeled source domain and fine-tuned the pre-trained model with pseudo labels generated by clustering samples on unlabeled target domain. Unfortunately, compared with supervised Re-ID methods, the performance of exiting clustering-based UDA Re-ID methods dropped sharply, since these methods utilized a not good enough baseline and seldom handled the noisy pseudo labels generated by clustering algorithm. In this paper, we try to address the UDA problem by designing a Strong Clustering-based Unsupervised Re-ID (SCURID) baseline for further research at first. Then, we integrate the hard labels and soft multilabels to learn more discriminative features on a unified angular regularized metric space. Specifically, we design the angular margin losses consisting of Hard Angular Margin Identification (HAMI) loss and Soft Angular Margin Identification (SAMI) loss. The HAMI loss can learn generalizable and discriminative features through hard pseudo labels generated by clustering on unlabeled target domain. The SAMI loss is proposed to refine the hard noisy pseudo labels through the soft multilabels obtained from peer network. Benefited from SCURID baseline and two angular margin losses, it enables the clustering-based UDA Re-ID model to alleviate the negative effect of noisy labels and toward more discriminability. Comprehensive and extensive experiments on three public available Re-ID datasets, e.g., Matket-1501, DukeMTMC-reID and MSMT17, demonstrate that our proposed method can outperform the state-of-the-art results on UDA Re-ID task. [ABSTRACT FROM AUTHOR]