  Sufficient training data normally is required to train deeply learned models.
However, due to the expensive manual process for labelling large number of
images, the amount of available training data is always limited. To produce
more data for training a deep network, Generative Adversarial Network (GAN) can
be used to generate artificial sample data. However, the generated data usually
does not have annotation labels. To solve this problem, in this paper, we
propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign
it to the generated data. With MpRL, the generated data will be used as the
supplementary of real training data to train a deep neural network in a
semi-supervised learning fashion. To build the corresponding relationship
between the real data and generated data, MpRL assigns each generated data a
proper virtual label which reflects the likelihood of the affiliation of the
generated data to pre-defined training classes in the real data domain. Unlike
the traditional label which usually is a single integral number, the virtual
label proposed in this work is a set of weight-based values each individual of
which is a number in (0,1] called multi-pseudo label and reflects the degree of
relation between each generated data to every pre-defined class of real data. A
comprehensive evaluation is carried out by adopting two state-of-the-art
convolutional neural networks (CNNs) in our experiments to verify the
effectiveness of MpRL. Experiments demonstrate that by assigning MpRL to
generated data, we can further improve the person re-ID performance on five
re-ID datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, VIPeR, and CUHK01.
The proposed method obtains +6.29%, +6.30%, +5.58%, +5.84%, and +3.48%
improvements in rank-1 accuracy over a strong CNN baseline on the five datasets
respectively, and outperforms state-of-the-art methods.
