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 ...

We extended the RNNLM and propose the Variational Auto-Encoder Recurrent Neural Network(VAE-RNNLM), which designed to explicitly capture such global features as continuous latent variable. Maximum ...

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 ...