2 research outputs found
Image Quality Assessment Techniques Show Improved Training and Evaluation of Autoencoder Generative Adversarial Networks
We propose a training and evaluation approach for autoencoder Generative
Adversarial Networks (GANs), specifically the Boundary Equilibrium Generative
Adversarial Network (BEGAN), based on methods from the image quality assessment
literature. Our approach explores a multidimensional evaluation criterion that
utilizes three distance functions: an score, the Gradient Magnitude
Similarity Mean (GMSM) score, and a chrominance score. We show that each of the
different distance functions captures a slightly different set of properties in
image space and, consequently, requires its own evaluation criterion to
properly assess whether the relevant property has been adequately learned. We
show that models using the new distance functions are able to produce better
images than the original BEGAN model in predicted ways.Comment: 10 pages, 7 figures, 2 table
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)