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...
RESULTS. This study presents a novel machine learning approach using multimodal vector-quantized variational autoencoders (VQ-VAEs) for predicting therapeutic target molecules across diseases. To address the lack of known therapeutic target鈥揹isease associations, we incorporate the information on ...
VQ-VAE-2(Vector Quantized Variational Autoencoder 2)是由DeepMind推出的一种自编码器模型,旨在解决生成模型在图像生成、语音生成和其他多模态任务中的问题。作为VQ-VAE的继任者,VQ-VAE-2在基础架构上进行了显著改进,尤其是在模型的多尺度结构和细节表达能力上。该模型结合了向量量化技术和变分自编码器(VAE)的优点...
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...
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 ...
VQ-GAN (Vector Quantized GAN) 是于 2020 年提出的生成对抗网络 (Generative Adversarial Network, GAN) 架构,这种模型架构建立在以下基础上:变分自编码器 (Variational Autoencoder, VAE)学习到的表示可以是离散的,而不仅是连续的。这种模型称为Vector Quantized VAE(VQ-VAE),能够用于生成高质量的图像,同时避免了...
Here we de- fine the loss between the initially generated glyph and its corresponding ground truth as: NC LiCnEit = wcmdℓ (zt,j , zˆt,j ) + ℓ (pt,j , pˆt,j ) , j=1 (5) where ℓ denotes the Cross-Entropy loss, all ...
This paper presents a finite-rate deep-learning (DL)-based channel state information (CSI) feedback method for massive multiple-input multiple-output (MIMO) systems. The presented method provides a finite-bit representation of the latent vector based on a vector-quantized variational autoencoder (...
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 ...
Anomaly detectionWe propose an out-of-distribution detection method that combines density and restoration-based approaches using Vector-Quantized Variational Auto-Encoders (VQ-VAEs). The VQ-VAE model learns to encode images in a categorical latent space. The prior distribution of latent codes is ...