就这样,我们知道了VQ-VAE是怎么生成离散编码的。VQ-VAE的编码器其实不会显式地输出离散编码,而是输出了多个「假嵌入」z_e(x)。之后,VQ-VAE对每个z_e(x)在嵌入空间里找最近邻,得到真正的嵌入z_q(x),把z_q(x)作为解码器的输入。 虽然我们现在能把编码器和解码器拼接到一起,但现在又多出了一个问题:怎...
VQ-VAE-2: RQ-VAE: 背景: 方法: 训练: Trick: FSQ: 方法: 实验: 引用: VAE: VAE (variational autoencoder,变分自编码器) 是一种强大的生成模型, Encoder 把数据编码到隐空间 z=Ecd(x) ,其学习条件概率 pϕ(z|x), Decoder 把数据从隐空间中重建回来 x=Dcd(z) ,其学习另一个条件概率 qθ(x...
近期，图像生成领域中出现了一种名为"codebook"机制的创新技术，这一概念最早在VQ-VAE论文中被提出。相较于传统的变分自编码器(VAE)，VQ-VAE利用codebook机制将图像编码为离散向量，为图像生成类任务提供了新思路。这一方法不仅启发了众多后续工作，如著名的Stable Diffusion，也为我们理解VQ-VAE的核心概念...
首先，他们训练一个PixelCNN，生成离散的「小图像」，这些小图像是VQ-VAE编码的基石。训练过程中，编码器将图像压缩成离散的「小图像」，解码器则负责还原。生成图像时，通过PixelCNN生成这些小图像，再通过VQ-VAE的解码环节完成生成。
要查看VQVAE(Vector Quantized Variational AutoEncoder)的codebook频率,你可以按照以下步骤进行操作: 加载VQVAE模型和codebook: 首先,你需要加载预训练的VQVAE模型和codebook。这通常涉及到加载模型的权重和codebook向量。假设你已经有了模型和codebook的保存路径,可以使用以下代码加载它们: python import torch # 加载VQVA...
In this paper, we present a simple alternative method for online codebook learning, Clustering VQ-VAE (CVQ-VAE). Our approach selects encoded features as anchors to update the ``dead'' codevectors, while optimising the codebooks which are alive via the original loss. This strategy brings ...
In this paper, we present a simple alterna- tive method for online codebook learning, Clustering VQ- VAE (CVQ-VAE). Our approach selects encoded features as anchors to update the "dead" codevectors, while optimis- ing the codebooks which are alive via the original loss. This strategy ...
We propose a Multi-Stage, Multi-Codebook (MSMC) approach to high-performance neural TTS synthesis. A vector-quantized, variational autoencoder (VQ-VAE) based feature analyzer is used to encode Mel spectrograms of speech training data by down-sampling progressively in multiple stages into MSMC Re...
2.1.4 VQ Codebook Learning The VQ autoencoder was originally introduced in the VQ- VAE [50] framework. It leverages a vector quantiza- tion codebook to address the problem of posterior col- lapse [14]. VQGAN further improves the visual quality of reconstructed images by introducing perceptual...
Frequent use of codebook resets to enhance the usage of the Vector Quantization Variational Autoencoder (VQ-VAE) may significantly alter the codebook distribution and consequently diminish the training efficiency. In this work, we introduce a novel codebook learning approach called Exponentially Weighted ...