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-...
学习变分自编码器(variational autocoder)再一次让我领略到了Bayesian理论的强大之处,variational autocoder是一种powerful的生成模型。 Limitations of autoencoders for content generation 在上期推送的经典的autoencoder中,存在一些局限性[1],如下 When thinking about it for a minute, this lack of structure among ...
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...
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,...
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. We observe that the standard variational approach in these models is unsuited for unsupervised clustering, and mit...
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。
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-...
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained ModelsCCPICLR 2024Pytorch P2OT: Progressive Partial Optimal Transport for Deep Imbalanced ClusteringP2OTICLR 2024Pytorch Deep Generative Clustering with Multimodal Diffusion Variational AutoencodersCMVAEICLR 2024To be released ...
(GMMs) with neural networks#Autoencoder-based clustering#Clustering deep neural networks (CDNN)#Generative adversarial networks (GANs)#Variational autoencoders (VAEs)#Applications of Deep Clustering#Image clustering#Text clustering#Speech and audio processing#Conclusions, Challenges and Future Directions#...
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 ...