Variational Autoencdoer. The Variational Autoencoder is a Generative Model. Its goal is to learn the distribution of a Dataset, and then generate new (unseen) data points from the same distribution.

Figure 2: Variational Autoencoder Architecture for the UCI Digits Dataset The key point is that a VAE learns the distribution of its source data rather than memorizing the source data. A data ...

Reproduction of the ICML 2018 publication Disentangled Sequential Autoencoder by Yinghen Li and Stephen Mandt, a Variational Autoencoder Architecture for learning latent representations of high ...

In this paper, we proposed a neural network (variational autoencoder) architecture that is used to generate an ECG corresponding to a single cardiac cycle. Our method generates synthetic ECGs using ...

Combining both approaches, we introduce a hybrid RQ-UNet variational autoencoder (RQUNet-VAE) scheme for image and time series decomposition used to reduce noise in satellite imagery. By including ...

A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...