Identity Retaining and Redundancy Reducing Gan for Person ReIdentification Person reidentification (ReID) models trained on one domain suffer performance degradation when tested on other domains. The existing works address this problem by domain translation with identity information preserving. However, these methods focused on adding pixel constraints to preserve identity, which also preserves a lot of redundant information. Therefore, this paper propose an identity retaining and redundancy reducing generative adversarial network (IRGAN), a domain translation method for person ReID. IRGAN is implemented by an unequalcycle strategy, which imposes both foreground and feature constraints to domain translation. By imposing partlevel feature constraints, the redundant information generated by pixel constraints can be reduced. Thus the performance of the domain translation is significantly improved. Experimental results indicate that our method is effective.

DomainCamera Adaptation for Unsupervised Person ReIdentification Although supervised person reidentification (ReID) performance has been significantly improved in recent years, it is still a challenge for unsupervised person ReIddue to its absence of labels across disjoint camera views. On the other hand, ReIdmodels trained on source domain usually offer poor performance when they are tested on target domain due to interdomain bias e.g. different classes and intradomain difference e.g camera variance. To overcome this problem, given a labeled source training domain and an unlabeled target training domain, we propose an unsupervised transfer method, DomainCamera Adaptation model, to generate a pseudo target domain by bridging interdomain bias and intradomain difference. The idea is to fill the absence of labels in target domain by transferring labeled images of source domain to target domain across cameras. Then we propose a crossdomain classification loss to extract discriminative representation across domains. The intuition is to think of unsupervised learning as semisupervised learning in target domain. We evaluate our deep model on Market1501 and DukeMTMCreID and the results show our model outperforms the stateofart unsupervised ReID methods by large margins.