Nov 2, 2017 · Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; …

在这篇文章中，我将先以易懂的逻辑带领大家一步一步领悟VQ-VAE的核心思想，再介绍VQ-VAE中关键算法的具体形式，最后把VQ-VAE的贡献及其对其他工作的影响做一个总结。 通过 …

The VQ VAE has the following fundamental model components: An Encoder class which defines the map x -> z_e; A VectorQuantizer class which transform the encoder output into a discrete …

Feb 9, 2021 · The fundamental difference between a VAE and a VQ-VAE is that VAE learns a continuous latent representation, whereas VQ-VAE learns a discrete latent representation. So …

VQ-VAE is a type of variational autoencoder that uses vector quantisation to obtain a discrete latent representation. It differs from VAEs in two key ways: the encoder network outputs …

Sep 27, 2023 · We propose to replace vector quantization (VQ) in the latent representation of VQ-VAEs with a simple scheme termed finite scalar quantization (FSQ), where we project the VAE …

VQ-VAE (Vector Quantized Variational Autoencoder) is a type of generative model that combines ideas from vector quantization and variational autoencoders. It was introduced in the paper …

Apr 3, 2021 · Let us dive into the author’s idea of learning discrete vectors in VQ-VAE. The encoder network encodes the image x into zₑ (x) and the decoder decodes the vector zq (x) …

In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs …

The Main Idea: VQ-VAE learns a discretized latent space, which is intuitively better-suited for discrete data such as images. It does this by introducing a powerful PixelCNN prior for the...