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 ...
VAE(Variational Autoencoder)假设样本x经由隐变量z生成,分为两步:1)从先验分布pθ(z)中采样隐变量z;2)从条件分布pθ(x|z)中采样样本x。生成模型的学习目标是使得数据的对数似然最大,即: θ∗=argmaxθ∑i=1nlogpθ(x(i)) 一个直观的pθ(x)的展开式为: pθ(x)=∫pθ(x,z)dz=∫p...
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...
Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representatio
Different flavors of VAE produce comparable results to GANs, for example, Vector Quantized Variational Autoencoder (VQ-VAE-2) (Razavi et al. 2019). Furthermore, combinations of VAEs and GANs are proposed by Larsen et al. (2016), Makhzani et al. (2015), Zamorski et al. (...
VAE系列 (1/3) 自动连播 20播放 简介 订阅合集 Variational Autoencoders Generative AI Animated 20:10 Vector-Quantized Variational Autoencoders(VQ-VAEs) DeepLearning 17:40 Variational Autoencoder-Model,ELBO,loss function and maths explained easily! 27:12 ...
called stochastically quantized variational autoencoder (SQ-VAE). In SQ-VAE, we observe a trend that the quantization is stochastic at the initial stage of the training but gradually converges toward a deterministic quantization, which we call self-annealing. Our experiments show that SQ-VAE improv...
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 ...
的,那么其聚类、提取特征都是容易的。提供了一些文献,其中有些 trick 讲如何训练这种不能微分的网络。还提到了Vector QuantizedVariationalAuto-encoder(VQVAE)。 Sequence as Embedding,做一个 seq2seq2seqauto-encoder。注意,这里举了一个例子,也是不可微分,使用强化学习进行训练。 此外,提了一些新研究 ...
I developed a neural audio codec model based on the residual quantized variational autoencoder architecture. I train the model on the Slakh2100 dataset, a standard dataset for musical source separation, composed of multi-track audio. The model can separate audio sources, achieving almost SoTA ...