Abstract
Generative Adversarial Networks (GANs) convergence in a high-resolution
setting with a computational constrain of GPU memory capacity (from 12GB to 24
GB) has been beset with difficulty due to the known lack of convergence rate
stability. In order to boost network convergence of DCGAN (Deep Convolutional
Generative Adversarial Networks) and achieve good-looking high-resolution
results we propose a new layered network structure, HDCGAN, that incorporates
current state-of-the-art techniques for this effect. A novel dataset, Curtó
Zarza (CZ), containing human faces from different ethnical groups in a wide
variety of illumination conditions and image resolutions is introduced. CZ is
enhanced with HDCGAN synthetic images, thus being the first GAN augmented face
dataset. We conduct extensive experiments on CelebA and CZ.
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