Contents
Why use VAE instead of ae?
A deep neural VAE is quite similar in architecture to a regular AE. The main difference is that the core of a VAE has a layer of data means and standard deviations. The means and standard deviations to representational values adds a variability that is missing from standard AEs.
Why use variational autoencoder instead of autoencoder?
variational autoencoders (VAEs) are autoencoders that tackle the problem of the latent space irregularity by making the encoder return a distribution over the latent space instead of a single point and by adding in the loss function a regularisation term over that returned distribution in order to ensure a better …
Are Autoencoders trained without supervision?
They are an unsupervised learning method, although technically, they are trained using supervised learning methods, referred to as self-supervised. Autoencoders are typically trained as part of a broader model that attempts to recreate the input.
What makes a VAE a variational autoencoder?
In a nutshell, a VAE is an autoencoder whose encodings distribution is regularised during the training in order to ensure that its latent space has good properties allowing us to generate some new data.
How are Variational autoencoders help solve latent space irregularity?
variational autoencoders (VAEs) are autoencoders that tackle the problem of the latent space irregularity by making the encoder return a distribution over the latent space instead of a single point and by adding in the loss function
How does an anomaly score work in autoencoder?
An anomaly score is designed to correspond to the reconstruction error. Autoencoder has a probabilistic sibling Variational Autoencoder(VAE), a Bayesian neural network. It tries not to reconstruct the original input, but the (chosen) distribution’s parameters of the output.
Can a VAE be used for anomaly detection?
The goal of this post is to introduce a probabilistic neural network (VAE) as a time series machine learning model and explore its use in the area of anomaly detection. As this post tries to reduce the math as much as possible, it does require some neural network and probability knowledge.