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ShapeEditer: a StyleGAN Encoder for Face Swapping
Face swapping using StyleGAN -
Mixture of Gaussian tasks
The dataset used in the paper is a mixture of Gaussian tasks with 9 and 16 modes. -
Dog StyleGAN2-ADA
Progress in GANs has enabled the generation of high-res-olution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such... -
AnimalFace StyleGAN2-ADA
Progress in GANs has enabled the generation of high-res-olution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such... -
Divergent-GAN for Positive Unlabeled learning
The proposed approach incorporates a biased PU risk into a generic GAN framework to guide the generator convergence towards the negative samples distribution. -
Logo Generation Using Regional Features: A Faster R-CNN Approach to Generativ...
Logo Generation Using Regional Features: A Faster R-CNN Approach to Generative Adversarial Networks -
Progressive growing of GANs
Progressive growing of GANs for improved quality, stability, and variation. -
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... -
GANs for Mobile Edge Networks
The dataset used in the paper is a Generative Adversarial Networks (GANs) dataset, which includes various GANs architectures and their applications in mobile edge networks. -
GIU-GANs:Global Information Utilization for Generative Adversarial Networks
Recently, with the rapid development of artificial intelligence, image generation based on deep learning has advanced significantly. Image generation based on Generative... -
Wasserstein1Benchmark
The dataset is used to test the performance of WGAN dual OT solvers. -
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Ne...
Font generation experiment using GlyphGAN, including legibility, diversity, and style consistency evaluation. -
A Style-Based Generator Architecture for Generative Adversarial Networks
A style-based generator architecture for generative adversarial networks. -
Many-to-many voice conversion using conditional cycle-consistent adversarial ...
Many-to-many voice conversion using conditional cycle-consistent adversarial networks -
Deep Convolutional Generative Adversarial Networks
The authors used three datasets: Large-scale Scene Understanding (LSUN), Imagenet-1k, and a newly assembled Faces dataset. -
Synthetic 2D dataset
Synthetic 2D dataset, an imbalanced mixture of 8 Gaussians -
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.
