Search Results for author:
Found 8 papers, 1 papers with code
Adversarially Slicing Generative Networks: Discriminator Slices Feature for One-Dimensional Optimal Transport
Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives.
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration
Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements.
Unsupervised vocal dereverberation with diffusion-based generative models
Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations.
A Versatile Diffusion-based Generative Refiner for Speech Enhancement
As the refiner, we train a diffusion-based generative model by utilizing a dataset consisting of clean speech only.
Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation
Score-based generative models learn a family of noise-conditional score functions corresponding to the data density perturbed with increasingly large amounts of noise.
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
In this paper, we propose a new training scheme that extends the standard VAE via novel stochastic dequantization and quantization, called stochastically quantized variational autoencoder (SQ-VAE).
Preventing Oversmoothing in VAE via Generalized Variance Parameterization
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative.
AR-ELBO: Preventing Posterior Collapse Induced by Oversmoothing in Gaussian VAE
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon that the learned latent space becomes uninformative.
