28 datasets found

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a tunable GAN, called α-GAN, to generate samples from a distribution.

Synthetic EEG data generated using Generative Adversarial Networks

The paper proposes a Geometry-Contrastive Generative Adversarial Network (GC-GAN) for transferring facial expressions across different subjects.

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...

OpenGAN: Open Set Generative Adversarial Networks

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.

Generative adversarial nets (GANs) are a class of deep learning models that consist of two neural networks: a generator and a discriminator.

Generative adversarial networks (GANs) are powerful gen-erative models, but usually suffer from instability and generalization problem which may lead to poor generations.

The dataset used in the paper is a 2D submanifold mixture of seven Gaussians arranged in a circle and embedded in 3D space.

PresGANs prevent mode collapse and are amenable to predictive log-likelihood evaluation.

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...

The dataset used in the paper is a synthetic dataset for testing the proposed Fake-As-Real GAN (FARGAN) method.

Dataset used for training and testing the DCGAN model

GANs trained on a dataset of GANs

The dataset used for training the StyleGAN2-ADA algorithm consists of high-resolution spatial data.

Face swapping using StyleGAN

The dataset used in the paper is a mixture of Gaussian tasks with 9 and 16 modes.

MelGAN: Generative adversarial networks for conditional waveform synthesis.

Logo Generation Using Regional Features: A Faster R-CNN Approach to Generative Adversarial Networks

Generative Adversarial Networks (GANs) consist of two networks: a generator G(z) and a discriminator D(x). The discriminator is trying to distinguish real objects from objects...