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. [2]

Mar 4, 2025 · Variational Autoencoders (VAEs) are generative models in machine learning (ML) that create new data similar to the input they are trained on. Along with data generation they also perform common autoencoder tasks like denoising. Like all autoencoders VAEs consist of: Encoder: Learns important patterns (latent variables) from input data.

Jun 12, 2024 · Variational autoencoders (VAEs) are generative models used in machine learning (ML) to generate new data in the form of variations of the input data they’re trained on. In addition to this, they also perform tasks common to other autoencoders, such as denoising.

Update 22/12/2021: Added support for PyTorch Lightning 1.5.6 version and cleaned up the code. A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility. The aim of this project is to provide a quick and simple working example for many of the cool VAE models out there.

Nov 19, 2022 · In contrast, a variational autoencoder (VAE) converts the input data to a variational representation vector (as the name suggests), where the elements of this vector represent different...

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.

A variational autoencoder (VAE) is one of several generative models that use deep learning to generate new content, detect anomalies and remove noise. VAEs first appeared in 2013, about the same time as other generative AI algorithms, such as generative adversarial networks ( GANs ) and diffusion models, but earlier than large language models ...

Aug 13, 2024 · Unlike traditional autoencoders that produce a fixed point in the latent space, the encoder in a VAE outputs parameters of a probability distribution—typically the mean and variance of a Gaussian distribution. This allows the VAE …

A variational autoencoder (VAE) is a type of neural network that learns to reproduce its input, and also map data to latent space. A VAE can generate samples by first sampling from the latent space. We will go into much more detail about what that …

Jun 6, 2019 · Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions. Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?)