  The main contribution of this paper is a simple semi-supervised pipeline that
only uses the original training set without collecting extra data. It is
challenging in 1) how to obtain more training data only from the training set
and 2) how to use the newly generated data. In this work, the generative
adversarial network (GAN) is used to generate unlabeled samples. We propose the
label smoothing regularization for outliers (LSRO). This method assigns a
uniform label distribution to the unlabeled images, which regularizes the
supervised model and improves the baseline. We verify the proposed method on a
practical problem: person re-identification (re-ID). This task aims to retrieve
a query person from other cameras. We adopt the deep convolutional generative
adversarial network (DCGAN) for sample generation, and a baseline convolutional
neural network (CNN) for representation learning. Experiments show that adding
the GAN-generated data effectively improves the discriminative ability of
learned CNN embeddings. On three large-scale datasets, Market-1501, CUHK03 and
DukeMTMC-reID, we obtain +4.37%, +1.6% and +2.46% improvement in rank-1
precision over the baseline CNN, respectively. We additionally apply the
proposed method to fine-grained bird recognition and achieve a +0.6%
improvement over a strong baseline. The code is available at
https://github.com/layumi/Person-reID_GAN.
