17 datasets found

The dataset used in this paper is FashionMNIST, CelebA, Cat vs Dog, and Imagenet.

The CelebA and CelebAHQ datasets are used in the paper for image generation.

The authors used MNIST and CelebA datasets for their experiments.

The dataset used in the paper is a MNIST dataset, a CIFAR10 dataset, and a CelebA dataset.

PuzzleCelebA is a dataset of 30K images of celebrities in High Definition (HD), with 80-20% train-test split.

CelebA and Chairs are the two datasets utilised by Higgins et al. (2016) for qualitative evaluation.

CelebA, FFHQ, and Teapots datasets for image generation.

The authors used the CIFAR10 and CelebA datasets for image modeling.

The CelebA-Mask-HQ dataset contains 30,000 HR faces with a size of 1024 × 1024 selected from the CelebA dataset.

CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset containing 10,177 celebrities images, each of which has 20 images.

The dataset used for super-resolution is DIV2K, CelebA, and Set5.

The MNIST, Fashion-MNIST, CIFAR-10, and CelebA datasets are used for image data.

The dataset used in the paper is CIFAR-10 and CIFAR-100 for image classification, and CelebA for image-to-image translation.

The dataset used in the paper is the CIFAR10 and CelebA datasets.

The datasets used in this paper are CelebA, LFWA, and RaFD.

The dataset used in this paper is the CIFAR-10, ImageNet and CelebA dataset.

Model inversion attacks are a type of privacy attack that reconstructs private data used to train a machine learning model, solely by accessing the model.