  Generative Adversarial Networks (GAN) are able to learn excellent
representations for unlabelled data which can be applied to image generation
and scene classification. Representations learned by GANs have not yet been
applied to retrieval. In this paper, we show that the representations learned
by GANs can indeed be used for retrieval. We consider heritage documents that
contain unlabelled Merchant Marks, sketch-like symbols that are similar to
hieroglyphs. We introduce a novel GAN architecture with design features that
make it suitable for sketch retrieval. The performance of this sketch-GAN is
compared to a modified version of the original GAN architecture with respect to
simple invariance properties. Experiments suggest that sketch-GANs learn
representations that are suitable for retrieval and which also have increased
stability to rotation, scale and translation compared to the standard GAN
architecture.
