In general, all autoencoders are a type of neural network capable of learning data. Autoencoders include both an encoder to compress input data into simpler elements and a decoder to reconstruct original data from its compressed elements. When implemented correctly, an autoencoder will ...
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.
Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representatio
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...
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之前是没有任何约束的 ...
Autonomous Intelligent Systems https://doi.org/10.1007/s43684-024-00065-x (2024) 4:8 Autonomous Intelligent Systems ORIGINAL ARTICLE Open Access Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives Marius Benkert1* , Michael ...
3.3.7 Importance Weighted AutoEncoder(IWAE) VAE Part VAE Review VAE is widely used. The structure is the neural network. Z is a latent variable. Analyzing the distribution of z helps us understand the original data X's inner low-dimensional representation and how to generate X. During the ...
Hence, this paper proposes a novel deep learning model named deep regularized variational autoencoder (DRVAE) for intelligent fault diagnosis of rotor-bearing system. Within the new model, the regular terms are respectively appended to the loss function of variational autoencoder (VAE) through ...
Learning and disentangling coherent latent representations of variational autoencoders (VAEs) have recently attracted widespread attention. However, the latent space of the VAE model is constrained by the prior distribution, which can hinder the latent variables from accurately capturing semantic information...
This paper presents a cloud-based approach to detect misbehavior in vehicular networks. Our method combines Gaussian Mixture Models and Variational Autoencoders in an FL setting using the VeReMi dataset, allowing each vehicle to train on its own data while sharing insights through a central ...