Jul 8, 2020 · We propose Nouveau VAE (NVAE), a deep hierarchical VAE built for image generation using depth-wise separable convolutions and batch normalization. NVAE is …

We propose Nouveau VAE (NVAE), a deep hierar-chical VAE built for image generation using depth-wise separable convolutions and batch normalization. NVAE is equipped with a residual …

Dec 6, 2020 · We propose Nouveau VAE (NVAE), a deep hierarchical VAE built for image generation using depth-wise separable convolutions and batch normalization. NVAE is …

NVAE is a deep hierarchical variational autoencoder that enables training SOTA likelihood-based generative models on several image datasets.

We introduce the Hierarchical Discrete Variational Autoencoder (HD-VAE): a hi-erarchy of variational memory layers. The Concrete/Gumbel-Softmax relaxation allows maximizing a …

NVAE builds upon traditional VAE architectures by introducing several key innovations that address fundamental challenges in training deep hierarchical VAEs. The model employs depth …

Jun 12, 2022 · Variational autoencoders (VAEs) are the generative models which use encoder-decoder type architecture to learn underlying features present in the dataset by means of …

In this section, we briey review the factorized hierarchical variational autoencoder (FHVAE) model and discuss the scalability issues of the original training objective.

Variational autoencoders were introduced to address different deficiencies of this architecture, which we will cover. The goal of variational autoencoders is to constrain the latent space of an …

The Hierarchical Variational Autoencoder (HVAE) builds upon traditional VAE architectures by employing a two-layered latent variable model that processes inputs through a bottom-up …