78 datasets found

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...

Propose an action recognition framework using Generative Adversarial Networks. Our model involves training a deep convolutional generative adversarial network (DCGAN) using a...

Recently, with the rapid development of artificial intelligence, image generation based on deep learning has advanced significantly. Image generation based on Generative...

The dataset used in this paper for testing the 3D-RecGAN++ model. It contains 1.5k SV and 2.5k CV testing datasets for each of the 6 categories.

A dataset for unsupervised person re-identification using Generative Adversarial Networks (GANs).

Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks

A style-based generator architecture for generative adversarial networks.

Synthetic 2D dataset, an imbalanced mixture of 8 Gaussians

Autoregressive models recently achieved comparable results versus state-of-the-art Generative Adversarial Networks (GANs) with the help of Vector Quantized Variational...

The dataset used for training the proposed Underwater GAN (UGAN) model, which consists of paired images of underwater scenes with and without distortion.

Generative Adversarial Networks (GANs) are an adversarial model that achieved impressive results on generative tasks. In spite of the relevant results, GANs present some...

The UCF101 dataset contains 13320 videos distributed in 101 action categories. This dataset is different from the above ones in that it contains mostly coarse sports activities...

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

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.

Training generative adversarial networks requires balancing of delicate adversarial dynamics. Even with careful tuning, training may diverge or end up in a bad equilibrium with...

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...