16 datasets found

Biomedical image analysis, data augmentation, Generative Adversarial Networks (GANs), synthetic images

Biomedical image analysis, data augmentation, Generative Adversarial Networks (GANs), synthetic images

The MedMNIST dataset is a large standardized biomedical image collection, including 12 number of 2D datasets and 6 number of 3D datasets.

Self-supervised and semi-supervised learning for GANs

Semi-supervised conditional GANs

The dataset used for training the Generative Adversarial Networks (GANs) for Super Mario Bros. and The Legend of Zelda.

The dataset used in this paper is a collection of images generated by different Generative Adversarial Networks (GANs). The dataset is used to evaluate the performance of GANs...

The Low-rank subspaces in GANs dataset is a collection of low-rank subspaces in GANs.

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...

Cells dataset is a highly imbalanced medical-image dataset, which contains one majority class and three minority classes.

MNIST Fashion and CIFAR-10 are two well-known balanced datasets, MNIST Fashion and CIFAR-10. We first sample 70% of images as the training set for generative models.

Generative adversarial networks (GANs) are one of the most powerful generative models, but always require a large and balanced dataset to train. Traditional GANs are not...

The dataset used for calibration during the quantization process.

This dataset is used in the paper to demonstrate the effectiveness of the GANSpace technique.

The dataset used in the paper for training and testing the proposed method for detecting, attributing and localizing GAN generated images.

High-fidelity image synthesis using Generative Adversarial Networks (GANs).