Nov 2, 2017 · 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 from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static.

Feb 9, 2021 · VQ-VAE stands for Vector Quantized Variational Autoencoder, that’s a lot of big words, so let’s first step back briefly and review the basics.

相比于普通的VAE，VQ-VAE能利用codebook机制把图像编码成离散向量，为图像生成类任务提供了一种新的思路。 VQ-VAE的这种建模方法启发了无数的后续工作，包括声名远扬的Stable Diffusion。 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。 通过阅读这篇文章，你不仅能理解VQ-VAE本身的原理，更能知道如何将VQ-VAE中的核心机制 …

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 discrete, rather than continuous, codes; and the prior is learnt rather than static.

To sample from the latent space, we fit a PixelCNN over the latent pixel values z_ij. The trick here is recognizing that the VQ VAE maps an image to a latent space that has the same structure as a 1 channel image.

Jun 2, 2019 · We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and …

Cannot retrieve latest commit at this time. A collection of resources and papers on Vector Quantized Variational Autoencoder (VQ-VAE) and its application. Understanding VQ-VAE (DALL-E Explained Pt. 1) How is it so good ? (DALL-E Explained Pt. 2) arXiv 2024. [Paper] CVPR 2023 Highlight. [Paper] CVPR 2023. [Paper] CVPR 2023. [Paper] CVPR 2023.

In this example, we develop a Vector Quantized Variational Autoencoder (VQ-VAE). VQ-VAE was proposed in Neural Discrete Representation Learning by van der Oord et al. In standard VAEs, the latent space is continuous and is sampled from a Gaussian distribution. It is generally harder to learn such a continuous distribution via gradient descent.

Aug 20, 2019 · This is a generative model based on Variational Auto Encoders (VAE) which aims to make the latent space discrete using Vector Quantization (VQ) techniques. This implementation trains a VQ-VAE based on simple convolutional blocks (no auto-regressive decoder), and a PixelCNN categorical prior as described in the paper.

Aug 28, 2024 · By combining the straight-through estimator with commitment loss, VQ-VAE successfully balances the need for discrete representations with the benefits of gradient-based optimization, enabling the model to learn rich, quantized embeddings that are both useful for downstream tasks and easy to optimize during training.