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...
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. (2020). The...
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 ...
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 ...
Vector quantized variational autoencoders, as variants of variational autoencoders, effectively capture discrete representations by quantizing continuous latent spaces and are widely used in generative tasks. However, these models still face limitations in handling complex image reconstruction, particularly in...
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 ...
Variational Autoencoder GAN (VAEGAN) 🥗 link link Vector Quantized VAE (VQVAE) link link Hamiltonian VAE (HVAE) link link Regularized AE with L2 decoder param (RAE_L2) link link Regularized AE with gradient penalty (RAE_GP) link link Riemannian Hamiltonian VAE (RHVAE) link link Hierarchical...
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 ...