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In this work we focused on GAN-based solution for the attribute guided facesynthesis. Previous works exploited GANs for generation of photo-realistic faceimages and did not pay attention to the question of diversity of the resultingimages. The proposed solution in its turn introducing novel latent space ofunit complex numbers is able to provide the diversity on the "birthday paradox"score 3 times higher than the size of the training dataset. It is important toemphasize that our result is shown on relatively small dataset (20k samples vs200k) while preserving photo-realistic properties of generated faces onsignificantly higher resolution (128x128 in comparison to 32x32 of previousworks).

High Diversity Attribute Guided Face Generation with GANs