Example showing how to define each of the main parameters of the VAE Architecture. Example showing how to change the image size (128x128) used while keeping the same latent representation (256x4x4) by changing the number of blocks.

A CNN Variational Autoencoder (CNN-VAE) implemented in PyTorch - sksq96/pytorch-vae

In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. [1] It is part of the families of probabilistic graphical models and variational Bayesian methods .

Mar 10, 2023 · To effectively extract the semantic features among words and distinguish polysemy in natural language, this paper proposes an intelligent text representation algorithm by combining the advantages of convolutional neural network …

Aug 16, 2024 · This notebook demonstrates how to train a Variational Autoencoder (VAE) (1, 2) on the MNIST dataset. A VAE is a probabilistic take on the autoencoder, a model which takes high dimensional input data and compresses it into a smaller representation.

Conditional VAE using CNN on MNIST in PyTorch. In order to run Variational autoencoder use train_vae.py and for Conditional Variational Autoencoder use train_cvae.py. Both of these two implementations use CNN.

变分自编码器 (Variational Autoencoder, VAE) 是一种结合了自编码器和概率建模的生成模型，通过编码器将输入数据映射到潜在空间中的概率分布，并通过解码器将从潜在空间采样得到的潜在变量映射回原始数据空间，实现了数据的生成和特征学习。

May 2, 2021 · This article discusses the basic concepts of VAE, including the intuitions behind the architecture and loss design, and provides a PyTorch-based implementation of a simple convolutional VAE to ...

May 14, 2020 · Autoencoders are a special kind of neural network used to perform dimensionality reduction. We can think of autoencoders as being composed of two networks, an encoder and a decoder. The encoder learns a non-linear transformation that projects the data from the original high-dimensional input space to a lower-dimensional latent space.

May 3, 2020 · Description: Convolutional Variational AutoEncoder (VAE) trained on MNIST digits.