-
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... -
OpenGAN: Open Set Generative Adversarial Networks
OpenGAN: Open Set Generative Adversarial Networks -
StyleVideoGAN
StyleVideoGAN: A Temporal Generative Model using a Pretrained StyleGAN -
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... -
2D Mixture of 8 Gaussians
The dataset used in the paper is a 2D mixture of 8 Gaussians evenly arranged in a circle. The generator has to search for 2D submanifolds in a 3D space. -
Training Generative Adversarial Networks via Primal-Dual Subgradient Methods
Synthetic data, 2D mixture of Gaussian data, MNIST dataset, CIFAR-10 dataset -
Generative Adversarial Nets
Generative adversarial nets (GANs) are a class of deep learning models that consist of two neural networks: a generator and a discriminator. -
Robust Generative Adversarial Network
Generative adversarial networks (GANs) are powerful gen-erative models, but usually suffer from instability and generalization problem which may lead to poor generations. -
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. -
Generative Adversarial Active Learning
Generative Adversarial Active Learning (GAAL) algorithm using Generative Adversarial Networks (GAN) for active learning by query synthesis. -
2D submanifold mixture of Gaussians in 3D
The dataset used in the paper is a 2D submanifold mixture of seven Gaussians arranged in a circle and embedded in 3D space. -
Prescribed Generative Adversarial Networks
PresGANs prevent mode collapse and are amenable to predictive log-likelihood evaluation. -
Speech enhancement generative adversarial network
Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those... -
Synthetic dataset for FARGAN
The dataset used in the paper is a synthetic dataset for testing the proposed Fake-As-Real GAN (FARGAN) method. -
DCGAN dataset
Dataset used for training and testing the DCGAN model -
SofGAN: A Portrait Image Generator with Dynamic Styling
A portrait image generator with dynamic styling, using a semantic occupancy field and a semantic instance-wise StyleGAN for regional texturing. -
StyleGAN2-ADA Training Dataset
The dataset used for training the StyleGAN2-ADA algorithm consists of high-resolution spatial data. -
Improved Techniques for Training GANs
The dataset used in the paper is a GAN training dataset. -
Simple Gradient Penalty µ-WGAN Optimization Problem
The dataset used in the paper is a simple gradient penalty µ-WGAN optimization problem (SGP µ-WGAN) with a simple gradient penalty term.
