作为Comate,由文心一言驱动的智能编程助手,我将为你详细解答关于Vector Quantized Variational Autoencoder(VQ-VAE)的问题。 1. 变分自编码器(Variational Autoencoder, VAE) 变分自编码器是一种生成模型,旨在学习数据的有效表示(编码)和从这些表示中生成新数据(解码)。与传统的自编码器不同,VAE通过引入潜在空间的概率...
Vector quantized variational autoencoder (VQ-VAE) has recently become an increasingly popular method in non-parallel zero-shot voice conversion (VC). The reason behind is that VQ-VAE is capable of disentangling the content and the speaker representations from the speech by using a content encoder...
VQ-VAE-2(Vector Quantized Variational Autoencoder 2)是由DeepMind推出的一种自编码器模型,旨在解决生成模型在图像生成、语音生成和其他多模态任务中的问题。作为VQ-VAE的继任者,VQ-VAE-2在基础架构上进行了显著改进,尤其是在模型的多尺度结构和细节表达能力上。该模型结合了向量量化技术和变分自编码器(VAE)的优点...
This is a PyTorch implementation of the vector quantized variational autoencoder (https://arxiv.org/abs/1711.00937). You can find the author'soriginal implementation in Tensorflow herewithan example you can run in a Jupyter notebook. Installing Dependencies ...
VQ-GAN (Vector Quantized GAN) 是于 2020 年提出的生成对抗网络 (Generative Adversarial Network, GAN) 架构,这种模型架构建立在以下基础上:变分自编码器 (Variational Autoencoder, VAE)学习到的表示可以是离散的,而不仅是连续的。这种模型称为Vector Quantized VAE(VQ-VAE),能够用于生成高质量的图像,同时避免了...
Here we de- fine the loss between the initially generated glyph and its corresponding ground truth as: NC LiCnEit = wcmdℓ (zt,j , zˆt,j ) + ℓ (pt,j , pˆt,j ) , j=1 (5) where ℓ denotes the Cross-Entropy loss, all ...
VQ VQ Encoder DecoderEncoder Decoder We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the auto... W Vq-Vae,A Razavi,AVD Oord 被引量: 0发表: 2019年 A Lightweight Restorative Adversarial Networ...
This paper presents a finite-rate deep-learning (DL)-based channel state information (CSI) feedback method for massive multiple-input multiple-output (MIMO) systems. The presented method provides a finite-bit representation of the latent vector based on a vector-quantized variational autoencoder (...
FactorVQVAE: Discrete latent factor model via vector quantized variational autoencoderDynamic latent factor modelVector quantizationAutoencoderTransformerPortfolio investmentThis study introduces FactorVQVAE, integrating VQVAE into dynamic factor modeling.A two-stage design extracts latent factors and models ...
Therefore, in this paper, we present an exhaustive study on using the Vector-Quantized Variational Autoencoder (VQ-VAE) to generate high-quality embeddings of the F0 curve. We experiment with various input transformations that focus on handling unvoiced regions of the F0, which are regions where...