Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High Diversity Attribute Guided Face Generation with GANs   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).

17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom