High-resolution Deep Convolutional Generative Adversarial Networks

Abstract

Generative Adversarial Networks (GANs) [Goodfellow et al. 2014] convergence in a high-resolution setting with a computational constrain of GPU memory capacity 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) [Radford et al. 2016] and achieve good-looking high-resolution results we propose a new layered network, HDCGAN, that incorporates current state-of-the-art techniques for this effect. Glasses, a mechanism to arbitrarily improve the final GAN generated results by enlarging the input size by a telescope ζ is also presented. A novel bias-free dataset, Curtó & Zarza, containing human faces from different ethnical groups in a wide variety of illumination conditions and image resolutions is introduced. Curtó is enhanced with HDCGAN synthetic images, thus being the first GAN augmented dataset of faces. We conduct extensive experiments on CelebA [Liu et al. 2015], CelebA-hq [Karras et al. 2018] and Curtó. HDCGAN is the current state-of-the-art in synthetic image generation on CelebA achieving a MS-SSIM of 0.1978 and a FRÉCHET Inception Distance of 8.44.

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Cited by in corpus (5)

• Styleformer: Transformer based Generative Adversarial Networks with Style Vector
• Mask Embedding in conditional GAN for Guided Synthesis of High Resolution Images
• Tractable loss function and color image generation of multinary restricted Boltzmann machine
• Doctor of Crosswise: Reducing Over-parametrization in Neural Networks
• Segmentation of Objects by Hashing