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Commodity prices dataset
Commodity prices dataset for testing state-of-the-art generative methods -
Hierarchical Exponential-family Energy-based (HEE) model on CIFAR10
The HEE model uses CIFAR10 to demonstrate its ability to generate high-quality images. -
Hierarchical Exponential-family Energy-based (HEE) model
The HEE model uses 2D synthetic datasets and FashionMNIST to validate its capabilities. -
EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and ...
Real EV charging dataset from a large tech company campus -
Generative Landmarks
The proposed method is capable of learning landmarks for various body regions, including faces and hands. The training set consists of roughly 18k frames (about 10 minutes each)... -
Geometry-Contrastive Generative Adversarial Network (GC-GAN) for Facial Expre...
The paper proposes a Geometry-Contrastive Generative Adversarial Network (GC-GAN) for transferring facial expressions across different subjects. -
Density Estimation Using Real NVP
This dataset has no description
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Alternating Back-Propagation for Generator Networks
This dataset has no description
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Wasserstein GAN
This dataset has no description
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Flexible Prior Distributions for Deep Generative Models
The dataset induced prior distribution is learned using a secondary GAN named PGAN. This prior is then used to further train the original GAN. -
Toy 2D dataset
The dataset used in the paper is a toy dataset consisting of 2D images. -
Anime128x128 dataset
The dataset used in the paper is a Anime128x128 dataset. -
CelebA128x128 dataset
The dataset used in the paper is a CelebA128x128 dataset. -
MNIST, CIFAR10, and CelebA datasets
The dataset used in the paper is a MNIST dataset, a CIFAR10 dataset, and a CelebA dataset. -
Optimal Transport Modeling
The dataset used in the paper is a noise distribution and a high-dimensional data distribution. -
Fast Cosmic Web Simulations with Generative Adversarial Networks
2D image snapshots from N-body simulations of size 500 Mpc and 100 Mpc -
Medical Image Generation using Generative Adversarial Networks
Generative adversarial networks (GANs) are unsupervised Deep Learning approach in the computer vision community which has gained significant attention from the last few years in... -
Sc-fegan: Face editing generative adversarial network with user's sketch and ...
Face editing dataset -
Factor Decomposed Generative Adversarial Networks for Text-to-Image Synthesis
Text-to-image synthesis using Factor Decomposed Generative Adversarial Networks (FDGAN)
