18 datasets found

A compact representation that combines a set of coarse shape proxies with learned neural textures to encode both medium and fine scale geometry as well as view-dependent...

The dataset used in the paper is a toy dataset consisting of 2D images.

The dataset used in the paper is a Anime128x128 dataset.

The dataset used in the paper is a CelebA128x128 dataset.

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

The dataset used in the paper is a noise distribution and a high-dimensional data distribution.

Generative adversarial nets (GANs) are a class of deep learning models that consist of two neural networks: a generator and a discriminator.

This dataset is used for 3D shape representation and generative modeling. It contains 3D voxel data.

The PointNet dataset is used for semantic segmentation of 3D point clouds. It contains annotated point clouds of objects such as table, human, box, robot, and cube.

The dataset used in the paper is a two-dimensional mixture of Gaussians.

Diffusion models currently stand as the predominant approach to generative modeling in audio and image domains.

We use the promoter DNA sequence dataset containing 100k promoter sequences with the corresponding transcription initiation signal profiles.

We apply SFM to diverse downstream discrete generation tasks across different domains including computer vision, natural language processing, and bioinformatics to demonstrate...

The dataset for the experiments comes from two dimensional turbulent flow simulated using the Lattice Boltzmann Method. The dataset consists of 1500 images of velocity fields,...

We use the preprocessed binarized MNIST dataset from [49] which has a split of 50k/10k/10k.

Word2Vec is a distributed word embedding generator that uses an artificial neural network to learn dense vector representations of words.

Dataset used for population synthesis and generative modeling.

Denoising diffusion models have become ubiquitous for generative modeling. The core idea is to transport the data distribution to a Gaussian by using a diffusion. Approximate...