This paper proposes Dirichlet Variational Autoencoder (DirVAE) using a Dirichlet prior. To infer the parameters of DirVAE, we utilize the stochastic gradient method by approximating the inverse cumulative distribution function of the Gamma distribution, which is a component of the Dirichlet distribution...
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...
一个流行的框架便是变分自动编码器(Variational Autoencoder, VAE)。VAEs 需要前提假设,但相较于 VAEs 能够模拟的复杂依赖关系而言这些假设引入的误差可以说微不足道。 1.1 隐含参数模型 如果要自动生成手写数字0-9,那么事先决定要生成什么数字是很有必要的。这个决定被称作隐含变量(latent variable)。隐含变量通常...
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...
Abstract In this paper we explore the effect of architectural choices on learning a variational autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for text where... 白话Variational Autoencoder(变分自编码器) ...
We present a variational autoencoder (ProteinVAE) that can generate synthetic viral vector serotypes without epitopes for pre-existing neutralizing antibodies. A pre-trained protein language model was incorporated into the encoder to improve data efficiency, and deconvolution-based upsampling was used ...
Variational autoencoders have encoders that compress input data into simpler elements, a decoder that reconstructs the original data from its compressed elements and a probabilistic latent space where each input data point is mapped to a distribution of points in the latent space. ...
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...
Paper Fully Spiking Variational Autoencoder Spiking neural networks (SNNs) can be run on neuromorphic devices with ultra-high speed and ultra-low energy consumption because of their binary and event-driven nature. Therefore, SNNs are expected to have various applications, including as generative ...
We propose a variational autoencoder which encodes and decodes directly to and from these parse trees, ensuring the generated outputs are always valid. Surprisingly, we show that not only does our model more often generate valid outputs, it also learns a more coherent latent space in which ...