  Generative adversarial networks (GANs) have demonstrated great success in
generating various visual content. However, images generated by existing GANs
are often of attributes (e.g., smiling expression) learned from one image
domain. As a result, generating images of multiple attributes requires many
real samples possessing multiple attributes which are very resource expensive
to be collected. In this paper, we propose a novel GAN, namely IntersectGAN, to
learn multiple attributes from different image domains through an intersecting
architecture. For example, given two image domains $X_1$ and $X_2$ with certain
attributes, the intersection $X_1 \cap X_2$ denotes a new domain where images
possess the attributes from both $X_1$ and $X_2$ domains. The proposed
IntersectGAN consists of two discriminators $D_1$ and $D_2$ to distinguish
between generated and real samples of different domains, and three generators
where the intersection generator is trained against both discriminators. And an
overall adversarial loss function is defined over three generators. As a
result, our proposed IntersectGAN can be trained on multiple domains of which
each presents one specific attribute, and eventually eliminates the need of
real sample images simultaneously possessing multiple attributes. By using the
CelebFaces Attributes dataset, our proposed IntersectGAN is able to produce
high quality face images possessing multiple attributes (e.g., a face with
black hair and a smiling expression). Both qualitative and quantitative
evaluations are conducted to compare our proposed IntersectGAN with other
baseline methods. Besides, several different applications of IntersectGAN have
been explored with promising results.
