Search Results for author: Toshimitsu Uesaka

Found 9 papers, 1 papers with code

Adversarially Slicing Generative Networks: Discriminator Slices Feature for One-Dimensional Optimal Transport

no code implementations30 Jan 2023 Yuhta Takida, Masaaki Imaizumi, Chieh-Hsin Lai, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji

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

no code implementations30 Jan 2023 Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon

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.

Blind Image Deblurring Denoising +1

Unsupervised vocal dereverberation with diffusion-based generative models

no code implementations8 Nov 2022 Koichi Saito, Naoki Murata, Toshimitsu Uesaka, Chieh-Hsin Lai, Yuhta Takida, Takao Fukui, Yuki Mitsufuji

Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations.

Regularizing Score-based Models with Score Fokker-Planck Equations

no code implementations9 Oct 2022 Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon

Score-based generative models learn a family of noise-conditional score functions corresponding to the data density perturbed with increasingly large amounts of noise.

Denoising

SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization

1 code implementation16 May 2022 Yuhta Takida, Takashi Shibuya, WeiHsiang Liao, Chieh-Hsin Lai, Junki Ohmura, Toshimitsu Uesaka, Naoki Murata, Shusuke Takahashi, Toshiyuki Kumakura, Yuki Mitsufuji

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).

Quantization

Preventing Oversmoothing in VAE via Generalized Variance Parameterization

no code implementations17 Feb 2021 Yuhta Takida, Wei-Hsiang Liao, Chieh-Hsin Lai, Toshimitsu Uesaka, Shusuke Takahashi, Yuki Mitsufuji

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

no code implementations1 Jan 2021 Yuhta Takida, Wei-Hsiang Liao, Toshimitsu Uesaka, Shusuke Takahashi, Yuki Mitsufuji

Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon that the learned latent space becomes uninformative.

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