于是我们去优化ELBO,相当于去maximize似然函数的下界,那么我们也间接地maximize 这个似然函数(是的,Variational Autoencoder本质上还是在去最大化似然函数)这也就是我们为什么要最大ELBO的原因。 然后这个ELBO也可以写为: ELBO(θ,ϕ)=−Ez∼qϕ(z∣x)[logPθ(x∣z)]+KL[qϕ(z∣x)‖P(z)] ...
在CV方向,目前最主流的生成模型是GAN(Generative Adversarial Network),在此之前其实是AE和VAE,也就是Autoencoder和Variational Autoencoder。GAN有诸多变形,但我个人认为最讲道理最理想的还是VAE-GAN以及其一系列的变形。因为在日常生活中,我们希望的并不是漫无目的的生成,而更多的是有保留的生成,希望能继承我们想输入...
Second paper:《Auto-encoding Variational Bayes》自编码变分贝叶斯的阅读笔记,程序员大本营,技术文章内容聚合第一站。
Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representation with the power of recent deep learning techniques. The main advantages of these types of generators lie in their ability to encode the informa...
Variational Autoencoder (VAE) is an outstanding model of them based on log-likelihood. In this paper, we propose a novel learnable prior, Pull-back Prior, for VAEs by adjusting the density of the prior through a discriminator that can assess the quality of data. It involves the ...
Adversarial Autoencoder(GAN和VAE的结合版) 阅读笔记 最近看了Adversarial Autoencoders(以下简称AAE)这篇paper,就随便写几句笔记。 paper链接,点我 1. 概述: GAN和VAE是近年来很火的生成模型(关于GAN,我之前写过几篇了,需要的话点击文末的相关链接即可),对于这两个模型的研究层出不穷,变体无数,而将这两者结...
VAE (variational autoencoder) Understanding Variational Autoencoders (VAEs) 为何不能用AE的decoder来直接生成数据? 因为这里的latent space的regularity无法保证 右边给出的例子,AE只是保证training过程中的cases的这些离散点,会导致严重的overfitting,你选中其他点的时候,不知道会发生什么,因为对于latent space之前是没...
Theneural networkarchitecture for the variational autoencoder was originally proposed in a 2013 paper by Diederik P. Kingma and Max Welling, titledAuto-Encoding Variational Bayes(link resides outside ibm.com). This paper also popularized what they called thereparameterization trick, an important machine...
(KPIs) of differently parameterized electrical machine topologies at the same time by mapping a high dimensional integrated design parameters in a lower dimensional latent space using a variational autoencoder. After training, via a latent space, the decoder and multi-layer neural network will ...
While AVI has enabled efficient training of deep generative models such as variational autoencoders (VAE), recent empirical work suggests that inference networks can produce suboptimal variational parameters. We propose a hybrid approach, to use AVI to initialize the variational parameters and run ...