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Circular Signal with Noise and Amplitude Variation
The dataset used in the paper is a set of true images and simulated images produced by a custom simulation technique. The true images are experimental data images, and the... -
First-Order Polynomial Images
The dataset used in the paper is a set of true images and simulated images produced by a custom simulation technique. The true images are experimental data images, and the... -
Student’s t-Generative Adversarial Networks
Generative Adversarial Networks (GANs) have a great performance in image generation, but they need a large scale of data to train the entire framework, and often result in... -
SinGAN: Learning a Generative Model from a Single Natural Image
SinGAN is a new unconditional generative model that can be learned from a single natural image. -
GIU-GANs:Global Information Utilization for Generative Adversarial Networks
Recently, with the rapid development of artificial intelligence, image generation based on deep learning has advanced significantly. Image generation based on Generative... -
A Style-Based Generator Architecture for Generative Adversarial Networks
A style-based generator architecture for generative adversarial networks. -
AmbientGAN: Generative models from lossy measurements
The AmbientGAN model adapts the original GAN configuration to handle cases with noisy or incomplete samples. -
LSUN-Church
Progress in GANs has enabled the generation of high-res-olution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such...
