  Most deep learning models are data-driven and the excellent performance is
highly dependent on the abundant and diverse datasets. However, it is very hard
to obtain and label the datasets of some specific scenes or applications. If we
train the detector using the data from one domain, it cannot perform well on
the data from another domain due to domain shift, which is one of the big
challenges of most object detection models. To address this issue, some
image-to-image translation techniques are employed to generate some fake data
of some specific scenes to train the models. With the advent of Generative
Adversarial Networks (GANs), we could realize unsupervised image-to-image
translation in both directions from a source to a target domain and from the
target to the source domain. In this study, we report a new approach to making
use of the generated images. We propose to concatenate the original 3-channel
images and their corresponding GAN-generated fake images to form 6-channel
representations of the dataset, hoping to address the domain shift problem
while exploiting the success of available detection models. The idea of
augmented data representation may inspire further study on object detection and
other applications.
