88 datasets found

The LAION Aesthetics V2 (L-Aes) dataset contains 0.22M image-text pairs.

The LAION-Aesthetics dataset is a large-scale dataset of images used for training and evaluating computer vision models.

The proposed model is built on the theoretical analysis presented in [30,14]. Similar to the ARAE [14], our model provides flexibility in learning an autoencoder from the input...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the two moons dataset for experiments.

This dataset is used for training and testing the BigGAN model.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors analyzed the generative mechanism of diffusion models and proposed a novel method...

The dataset used for language modeling, disentangled representation learning, and image generation.

The dataset used in the paper for testing the proposed Data-Independent Operator (DIO) framework for generalizable forgery image detection.

The dataset used in the paper is a synthetic MNIST dataset generated by forming barycenters constructed with weights sampled uniformly from ∆3.

Wasserstein Auto-Encoder (WAE) is a generative model that uses a combination of convolutional and fully connected layers to learn a probabilistic representation of images.

The dataset used in the paper is the LSUN bedrooms dataset, a large-scale image dataset.

The proposed framework generates JPEG compressed images using generative adversarial networks.

A large-scale dataset for generalizable and robust synthetic image detection, comprising diverse generators, object categories, and real-world challenges.

The InfiMNIST dataset is a synthetic dataset of MNIST-like images.

The dataset used in this paper is a collection of images generated by different Generative Adversarial Networks (GANs). The dataset is used to evaluate the performance of GANs...

Conditional image generation has paved the way for several breakthroughs in image editing, generating stock photos and 3-D object generation.

Dataset used for training and testing the DCGAN model

The MNIST and FashionMNIST datasets are used to test the performance of the proposed generative autoencoders.

The dataset used for the text-to-image task consists of 20 context tokens, to which three fixed tokens have been concatenated, representing the static context "the picture of".

The proposed method uses the FFHQ, AFHQ, and LSUN datasets for image generation tasks.