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Pneumoniamnist
Biomedical image analysis, data augmentation, Generative Adversarial Networks (GANs), synthetic images -
Self-supervised and semi-supervised learning for GANs
Self-supervised and semi-supervised learning for GANs -
Semi-supervised conditional GANs
Semi-supervised conditional GANs -
GAN Training Data
The dataset used for training the Generative Adversarial Networks (GANs) for Super Mario Bros. and The Legend of Zelda. -
GAN datasets
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... -
Low-rank subspaces in GANs
The Low-rank subspaces in GANs dataset is a collection of low-rank subspaces in GANs. -
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... -
MNIST Fashion & CIFAR-10
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. -
Improved Balancing GAN: Minority-class Image Generation
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... -
StyleGAN2-ADA
The dataset used for calibration during the quantization process. -
MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity ...
High-fidelity image synthesis using Generative Adversarial Networks (GANs).
