  Person re-identification (Re-ID) poses a unique challenge to deep learning:
how to learn a deep model with millions of parameters on a small training set
of few or no labels. In this paper, a number of deep transfer learning models
are proposed to address the data sparsity problem. First, a deep network
architecture is designed which differs from existing deep Re-ID models in that
(a) it is more suitable for transferring representations learned from large
image classification datasets, and (b) classification loss and verification
loss are combined, each of which adopts a different dropout strategy. Second, a
two-stepped fine-tuning strategy is developed to transfer knowledge from
auxiliary datasets. Third, given an unlabelled Re-ID dataset, a novel
unsupervised deep transfer learning model is developed based on co-training.
The proposed models outperform the state-of-the-art deep Re-ID models by large
margins: we achieve Rank-1 accuracy of 85.4\%, 83.7\% and 56.3\% on CUHK03,
Market1501, and VIPeR respectively, whilst on VIPeR, our unsupervised model
(45.1\%) beats most supervised models.
