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Autoencoder& GANs for Imbalanced Multi-Omics
The proposed model is applied to two publicly available datasets, the first is the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) dataset, which contains DNA... -
Mixture of Gaussian tasks
The dataset used in the paper is a mixture of Gaussian tasks with 9 and 16 modes. -
Efficient GAN-based Anomaly Detection
Efficient GAN-based anomaly detection. -
Large Logo Dataset (LLD)
The Large Logo Dataset (LLD) is a large-scale logo dataset crawled from the web, containing 486,377 logos in 32x32 pixel resolution. -
GANs and diffusion models dataset
The dataset used in the paper for synthetic image detection, aiming to detect forgery images from diverse generative methods. -
GANs for Anomaly Detection
Anomaly detection using GANs is an emerging research field. Anomaly detection using GANs is an emerging research field. Detecting and correctly classifying something unseen as... -
Low-rank subspaces in GANs
The Low-rank subspaces in GANs dataset is a collection of low-rank subspaces in GANs. -
Adversarial Feature Learning
Adversarial Feature Learning -
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. -
MNIST, Fashion-MNIST, CIFAR-10, and CelebA
The dataset used in the paper is not explicitly described, but it is mentioned that the authors pre-trained GANs on four datasets: MNIST, Fashion-MNIST, CIFAR-10, and CelebA. -
Diffusion Models Beat GANs on Image Synthesis
Diffusion models have recently emerged as the state-of-the-art of generative modeling, demonstrating remarkable results in image synthesis and across other modalities. -
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Ne...
Font generation experiment using GlyphGAN, including legibility, diversity, and style consistency evaluation. -
Synthetic Data
The dataset used in the paper is a synthetic dataset for off-policy contextual bandits, with contexts x ∈ X, a finite set of actions A, and bounded real rewards r ∈ A → [0, 1]. -
Synthetic 2D dataset
Synthetic 2D dataset, an imbalanced mixture of 8 Gaussians
