DEEP UNSUPERVISED CLUSTERING WITH GAUSSIAN MIXTURE VARIATIONAL AUTOENCODERS(ICLR2017) 技术标签:读论文深度学习 文章目录 写在前面 摘要 1. Introduction部分 2. VAE的回顾 3. GMVAE 3.1 生成模型 3.2 推理网络 3.2.1 条件先验项 3.3 z先验项 3.4 过度正则化问题
1. 引言 这篇博文主要是对论文“Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embedding”的整理总结,这篇文章将图嵌入与概率深度高斯混合模型相结合,使网络学习到符合全局模型和局部结构约束的强大特征表示。将样本作为图上的节点,并最小化它们的后验分布之间的加权距离,在这里使用Jenson-...
Basic architechture of variational autoencoder Unlike classical (sparse, denoising, etc.) autoencoders, Variational autoencoders (VAEs) are generative models, like Generative Adversarial Networks 首先需要明确的是variational autocoder是一种典型的生成模型,而传统的autoencoder则不是。 与经典的autoencoder的不...
4.2 基于变分自编码器(Variational AutoEncoder, VAE)的深度聚类 参考:变分推断与变分自编码器,变分深度嵌入(Variational Deep Embedding, VaDE) ,基于图嵌入的高斯混合变分自编码器的深度聚类(Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embedding, DGG),元学习——Meta-Amortized Variation...
In every training epoch, the K-means clustering algorithm is first implemented using the hidden outputs of Φ(⋅) (i.e., feature encoder) to determine the pseudo-labels for the cluster classifier. Then, the model is jointly trained to optimize a multitask loss function that combines the ...
Generative Adversarial Network (GAN) and Variational Autoencoder (VAE) 2. loss function 引导网络学习适合聚类的表示能力(representation),我们将loss分成两类:主聚类损失(principal clustering loss)和辅助聚类损失(auxiliary clustering loss) Principal Clustering Loss 这类聚类丢失函数包含样本的聚类中心化和聚类分配...
生成模型——NVAE: A Deep Hierarchical Variational Autoencoder——arxiv2020.07,程序员大本营,技术文章内容聚合第一站。
Integrating 4 high-dimensional datasets comprising metabolites from plasma and microRNAs from plasma, synovial fluid, or urine, a multimodal deep learning variational autoencoder architecture with K-means clustering was employed. Features influencing cluster assignment were identified and pathway analyses ...
The 2-dimensional Ising model on a square lattice is investigated with a variational autoencoder in the non-vanishing field case for the purpose of extracting the crossover region between the ferromagnetic and paramagnetic phases. The encoded latent variable space is found to provide suitable metrics...
参考:聚类——GMM,基于图嵌入的高斯混合变分自编码器的深度聚类(Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embedding, DGG)- 凯鲁嘎吉 - 博客园 3.5 基于互信息的深度聚类 参考:COMPLETER: 基于对比预测的缺失视图聚类方法,Meta-RL——Decoupling Exploration and Exploitation for Meta-...