7 datasets found

The HEE model uses CIFAR10 to demonstrate its ability to generate high-quality images.

LatentGAN Autoencoder: Learning Disentangled Latent Distribution

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...

Generative Autoencoding of Dropout Patterns

The dataset used for training and validation of the proposed approach to combine semantic segmentation and dense outlier detection.

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

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...