1 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