Implementation of Gaussian Mixture Variational Autoencoder (GMVAE) for Unsupervised Clustering in PyTorch and Tensorflow. The probabilistic model is based on the model …

VAE estimates latent variable（x） and sends latent variables（x） to GMM. GMM clusters latent variables（x） sent from VAE and sends mean and variance parameters of the Gaussian …

本文我将介绍 VAE 针对 无监督聚类 的一个扩展： GMVAE，即基于 高斯混合模型 的VAE。 我们在 之前的文章 中已经介绍了VAE，它是一个无监督的生成模型，其良好的性能和end-to-end …

Nov 8, 2016 · We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through …

This project implements a powerful Variational Autoencoder (VAE) with Gaussian Mixture as a prior distribution, enabling both unsupervised clustering and high-quality image generation. …

Jun 12, 2018 · A variational autoencoder (VAE) provides a way of learning the probability distribution $p(x,z)$ relating an input $x$ to its latent representation $z$. In particular, the …

Oct 27, 2019 · Abstract: We propose DGG: {\textbf D}eep clustering via a {\textbf G}aussian-mixture variational autoencoder (VAE) with {\textbf G}raph embedding. To facilitate clustering, …

Mar 14, 2022 · variant of VAE with GMM as prior. goal : unsupervised clustering via DGM; Problem of regular VAE : over-regularisation \(\rightarrow\) leads to cluster degeneracy. …

Jan 12, 2021 · In this paper, we propose a principled unsupervised meta-learning model, namely Meta-GMVAE, based on Variational Autoencoder (VAE) and set-level variational inference.

We study a variant of the variational autoencoder model with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models.