Nov 20, 2020 · We test if insufficient depth explains why by scaling a VAE to greater stochastic depth than previously explored and evaluating it CIFAR-10, ImageNet, and FFHQ.

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 …

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 …

Repository for the paper "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images" (https://arxiv.org/abs/2011.10650) Some model samples and a …

Mar 17, 2022 · In this paper, we introduce VDVAE-SR, a new model that aims to exploit the most recent deep VAE methodologies to improve upon the results of similar models. VDVAE-SR …

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 …

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 …

Nov 20, 2020 · We present a hierarchical VAE that, for the first time, outperforms the PixelCNN in log-likelihood on all natural image benchmarks. We begin by observing that VAEs can actually …

NVAE is a deep hierarchical variational autoencoder that enables training SOTA likelihood-based generative models on several image datasets. NVAE is built in Python 3.7 using PyTorch …

Efficient-VDVAE is a memory and compute efficient very deep hierarchical VAE. It converges faster and is more stable than current hierarchical VAE models. It also achieves SOTA …