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Hierarchical Exponential-family Energy-based (HEE) model on CIFAR10
The HEE model uses CIFAR10 to demonstrate its ability to generate high-quality images. -
Hierarchical Exponential-family Energy-based (HEE) model
The HEE model uses 2D synthetic datasets and FashionMNIST to validate its capabilities. -
Density Estimation Using Real NVP
This dataset has no description
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Alternating Back-Propagation for Generator Networks
This dataset has no description
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Wasserstein GAN
This dataset has no description
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Flexible Prior Distributions for Deep Generative Models
The dataset induced prior distribution is learned using a secondary GAN named PGAN. This prior is then used to further train the original GAN. -
Improved Precision and Recall Metric for Assessing Generative Models
The dataset used in the paper is not explicitly described, but it is mentioned that it is a generative model dataset. -
A Bayesian Non-parametric Approach to Generative Models
Generative models have emerged as a promising technique for producing high-quality im-ages that are indistinguishable from real images. -
SPI-GAN: DENOISING DIFFUSION GANS WITH STRAIGHT-PATH INTERPOLATIONS
Score-based generative models (SGMs) show the state-of-the-art sampling quality and diversity. However, their training/sampling complexity is notoriously high due to the highly... -
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...
