  Most existing person re-identification (re-id) methods require supervised
model learning from a separate large set of pairwise labelled training data for
every single camera pair. This significantly limits their scalability and
usability in real-world large scale deployments with the need for performing
re-id across many camera views. To address this scalability problem, we develop
a novel deep learning method for transferring the labelled information of an
existing dataset to a new unseen (unlabelled) target domain for person re-id
without any supervised learning in the target domain. Specifically, we
introduce an Transferable Joint Attribute-Identity Deep Learning (TJ-AIDL) for
simultaneously learning an attribute-semantic and identitydiscriminative
feature representation space transferrable to any new (unseen) target domain
for re-id tasks without the need for collecting new labelled training data from
the target domain (i.e. unsupervised learning in the target domain). Extensive
comparative evaluations validate the superiority of this new TJ-AIDL model for
unsupervised person re-id over a wide range of state-of-the-art methods on four
challenging benchmarks including VIPeR, PRID, Market-1501, and DukeMTMC-ReID.
