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
This study introduces FactorVQVAE, the first integration of the Vector Quantized Variational Autoencoder (VQVAE) into factor modeling, providing a novel framework for predicting cross-sectional stock returns and constructing systematic investment portfolios. The model employs a two-stage architecture to ...
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...
#ai创造营# 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...
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...
The Vector-Quantized Variational AutoEncoder (VQ-VAE) is the foundation of the proposed method. The VQ-VAE model is trained to learn the non-linear mapping of degraded panchromatic image patches to high-resolution patches. This approach ensures that high-resolution patches can be recovered from ...
Anomaly detectionWe propose an out-of-distribution detection method that combines density and restoration-based approaches using Vector-Quantized Variational Auto-Encoders (VQ-VAEs). The VQ-VAE model learns to encode images in a categorical latent space. The prior distribution of latent codes is ...
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...
In this paper, we propose a novel Decomposed Vector-Quantized Variational Autoencoder(DVQ-VAE) to address this limitation by decomposing hand into several distinct parts and encoding them separately. This part-aware decomposed architecture facilitates more precise management of the interaction between ...