Resynthesis

12 papers with code • 2 benchmarks • 2 datasets

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Most implemented papers

Detecting the Unexpected via Image Resynthesis

cvlab-epfl/detecting-the-unexpected ICCV 2019

In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes can appear at test time.

Generative Spoken Language Modeling from Raw Audio

pytorch/fairseq 1 Feb 2021

We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation.

Speech Resynthesis from Discrete Disentangled Self-Supervised Representations

facebookresearch/speech-resynthesis 1 Apr 2021

We propose using self-supervised discrete representations for the task of speech resynthesis.

Unifying Probabilistic Models for Time-Frequency Analysis

wil-j-wil/unifying-prob-time-freq 6 Nov 2018

In audio signal processing, probabilistic time-frequency models have many benefits over their non-probabilistic counterparts.

Coordinate-based Texture Inpainting for Pose-Guided Image Generation

dolorousrtur/coordinate_based_inpainting 28 Nov 2018

Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body.

On Adversarial Mixup Resynthesis

christopher-beckham/amr NeurIPS 2019

In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders.

GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks

zhangqianhui/GazeCorrection arXiv 2019

Gaze correction aims to redirect the person's gaze into the camera by manipulating the eye region, and it can be considered as a specific image resynthesis problem.

Parametric Resynthesis with neural vocoders

r9y9/wavenet_vocoder 16 Jun 2019

We propose to utilize the high quality speech generation capability of neural vocoders for noise suppression.

Spectral Processing of COVID-19 Time-Series Data

abstract-theory/Spectral-Processing-COVID-19 13 Aug 2020

The presence of oscillations in aggregated COVID-19 data not only raises questions about the data's accuracy, it hinders understanding of the pandemic.

Dynamical Variational Autoencoders: A Comprehensive Review

XiaoyuBIE1994/DVAE 28 Aug 2020

The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space learned in an unsupervised manner.