  In this work, we present a novel approach for training Generative Adversarial
Networks (GANs). Using the attention maps produced by a Teacher- Network we are
able to improve the quality of the generated images as well as perform weakly
object localization on the generated images. To this end, we generate images of
HEp-2 cells captured with Indirect Imunofluoresence (IIF) and study the ability
of our network to perform a weakly localization of the cell. Firstly, we
demonstrate that whilst GANs can learn the mapping between the input domain and
the target distribution efficiently, the discriminator network is not able to
detect the regions of interest. Secondly, we present a novel attention transfer
mechanism which allows us to enforce the discriminator to put emphasis on the
regions of interest via transfer learning. Thirdly, we show that this leads to
more realistic images, as the discriminator learns to put emphasis on the area
of interest. Fourthly, the proposed method allows one to generate both images
as well as attention maps which can be useful for data annotation e.g in object
detection.
