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Joint Observations
The dataset used in the paper is a set of joint observations that are used to train the generative model. The dataset is used to evaluate the performance of the proposed method... -
Multi-Agent Environment
The dataset used in the paper is a multi-agent environment where agents learn to coordinate their actions to achieve a common goal. The dataset is used to evaluate the proposed... -
Learning (Very) Simple Generative Models Is Hard
The dataset is used to study the computational complexity of learning pushforwards of Gaussians under one-hidden-layer ReLU networks. -
Variational Discriminator Bottleneck
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a 34 degrees-of-freedom humanoid character and a phase-functioned... -
Source Separation with Deep Generative Priors
Source Separation with Deep Generative Priors -
Generative Models for 3D Objects
Generative models for 3D objects -
GAN and VAE from an Optimal Transport Point of View
The dataset used in the paper is a generative model, specifically a Wasserstein GAN and a Wasserstein VAE. -
Probabilistic Programming
Probabilistic programming languages (PPLs) allow for automatic inference about random variables in generative models written as programs. -
Max-Margin Deep Generative Models
Deep generative models (DGMs) are effective on learning multilayered represen- tations of complex data and performing inference of input data by exploring the generative ability. -
splitMNIST, splitFashionMNIST, splitCIFAR
The dataset used in the paper is a continual learning benchmark, consisting of three datasets: splitMNIST, splitFashionMNIST, and splitCIFAR. -
GenCO: Generating Diverse Designs with Combinatorial Constraints
GenCO: Generating Diverse Designs with Combinatorial Constraints -
BigGAN-Deep
This dataset is used for training and testing the BigGAN model. -
Generative-based fusion mechanism for multi-modal tracking
Generative-based fusion mechanism for multi-modal tracking -
Projected Generative Diffusion Models
The dataset used in the paper is a large dataset of images, videos, and other data used for training and testing the Projected Generative Diffusion Models (PGDM) and other... -
Generalized Diffusion with Learnable Encoding-Decoding
The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used the Yelp review dataset and the bookcorpus dataset. -
Solar Power Dataset
The dataset consists of real-life solar power production data. -
Spiral Dataset
The dataset used in the paper is a synthetic dataset consisting of points in 2D that follow a spiral distribution. -
Diffusion Random Feature Model
Diffusion probabilistic models have been successfully used to generate data from noise. However, most diffusion models are computationally expensive and difficult to interpret... -
Generative Model Evaluation
The dataset used in this paper for generative model evaluation, consisting of 4.1M images from 41 state-of-the-art generative models spanning diffusion models, GANs, variational...
