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 equipped with a residual parameterization of Normal distributions and …

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 graphical models and variational Bayesian methods. [2]

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 visualization of how it generates them: This repository is tested with PyTorch 1.6, CUDA 10.1, Numpy 1.16, Ubuntu 18.04, and V100 GPUs.

本文介绍发表在 ICLR 2021 上的 Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images. 这篇文章非常精彩，论证了增加 VAE 的层数能够在保证生成速度的情况下，实现超过自回归模型 (AR) 的 NLL (negative log-likelihood). 具体来说，文章分析了为什么更多层数的 VAE 能超过 AR, 如何在增加 VAE 层数的时候保证生成速度，究竟需要多少层，能好多少，以及好在什么方面。 首先，我们 motivate 为什么要研究 VAE 的层数。

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 parameterization of Normal distributions and its training is stabilized by spectral regularization.

Mar 23, 2023 · We present fast, realistic image generation on high-resolution, multimodal datasets using hierarchical variational autoencoders (VAEs) trained on a deterministic autoencoder's latent space. In this two-stage setup, the autoencoder compresses the image into its semantic features, which are then modeled with a deep VAE.

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 equipped with a residual parameterization of Normal distributions and …

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 likelihood-based performance on several image datasets.

PyTorch implementation of Very Deep VAE (VD-VAE) from "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images"