一、普通的auto-encoder auto-encoder分为encoder,latent vector,decoder三个部分,通过encoder(全连接,卷积,RNNs等)将一些图片,语言等高维的数据映射到低维的数据,得到压缩后的或者说是降维后的表达,也就是latent vector。decoder一般来说与encoder对称的一个结构,当然也可以是另外的结构。但是实现的功能只有一个,就...
In this paper we propose a method to compress 3D voxel grids using an octree representation and Variational Autoencoders (VAEs). We first capture a 3D voxel grid -in our application with collaborating Realsense D435 and T265 cameras. The voxel grid is decomposed into three types of octants...
Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representatio
Lecture 4 Latent Variable Models -- Variational AutoEncoder (VAE) While the old way of doing statistics used to be mostly concerned with inferring what has happened, modern statistics is more concerned with predicting what will happen, and many practical machine learning applications rely on it. ...
Variational autoencoders (VAEs) are generative models used in machine learning to generate new data samples as variations of the input data they’re trained on.
First, a new model of Variational Autoencoders (VAEs) with a Gaussian Random Field (GRF) prior is presented. This offers a relevant way to model images with strong spatial correlations. Second, the VAE-GRF is used in the context of Anomaly Detection (AD). More precisely, we address the...
Thus, the variational autoencoder with prior concept transformation (PT-VAE) attempts to minimise this variational lower bound. We also show that this prior concept presents a better lower bound of the PT-VAE, which enables us to obtain better disentangled representations for improved clustering ...
Hybrid Convolutional Variational Autoencoder for TextGeneration paer reading,程序员大本营,技术文章内容聚合第一站。
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 for decoding to ...
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 ...