  In this work we focused on GAN-based solution for the attribute guided face
synthesis. Previous works exploited GANs for generation of photo-realistic face
images and did not pay attention to the question of diversity of the resulting
images. The proposed solution in its turn introducing novel latent space of
unit 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 to
emphasize that our result is shown on relatively small dataset (20k samples vs
200k) while preserving photo-realistic properties of generated faces on
significantly higher resolution (128x128 in comparison to 32x32 of previous
works).

Computer Vision and Pattern Recognition