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Mixture of Gaussians, CIFAR-10, STL-10, CelebA, and ImageNet
The dataset used in the paper is a mixture of Gaussians, CIFAR-10, STL-10, CelebA, and ImageNet. -
ProGAN and LSUN
ProGAN and LSUN are used as training datasets for the FakeInversion model. -
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... -
Street View House Numbers (SVHN) dataset
Generative Adversarial Networks (GANs) are an adversarial model that achieved impressive results on generative tasks. In spite of the relevant results, GANs present some... -
AmbientGAN: Generative models from lossy measurements
The AmbientGAN model adapts the original GAN configuration to handle cases with noisy or incomplete samples. -
Distributional Multivariate Policy Evaluation and Exploration with the Bellma...
The dataset is used to evaluate the distributional approach to reinforcement learning (DiRL) and its equivalence to Generative Adversarial Networks (GANs). -
LSUN-Church
Progress in GANs has enabled the generation of high-res-olution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such... -
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). -
LOGAN: Latent Optimisation for Generative Adversarial Networks
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...
