Disentangling the Latent Space: A Guide to Beta-VAE
Autoencoders are a type of neural network that can be used to learn a compressed representation of input data. They work by training the network to reconstruct the input data from a lower-dimensional latent representation, which is typically obtained using an encoder. Autoencoders are versatile and can be used for a variety of tasks, including data compression, anomaly detection, and feature learning high-dimensional data using a neural network model with a narrow bottleneck layer in the middle (oops, this is probably not true for Variational Autoencoder, and we will investigate it in details in later sections). A nice byproduct is dimension reduction: the bottleneck layer captures a compressed latent encoding....