VAE: Variational Autoencoder Variational Autoencoders (VAEs) address the limitations of traditional autoencoders by enabling the generation of new data samples. They aim to learn a latent space ...

Variational Autoencoders (VAEs) are a type of generative model that extends traditional autoencoders by adding a probabilistic spin to their latent space representation. Unlike traditional ...

Variational Autoencoders (VAEs) are generative models used for unsupervised learning. They consist of an encoder that maps input data to a probabilistic distribution in a lower-dimensional latent ...

Recently, a generative variational autoencoder (VAE) has been proposed for speech enhancement to model speech statistics. However, this approach only uses clean speech in the training phase, making ...

A variational autoencoder produces a probability distribution for the different features of the training images/the latent attributes. When training, the encoder creates latent distributions for the ...

A variational autoencoder (Kingma and Welling, 2013; Doersch, 2016) consists of an encoder and a decoder. We propose the following architecture for them. The encoder consists of a convolutional and a ...