Autoencoders are a type of neural network architecture used in unsupervised machine learning for tasks such as dimensionality reduction, feature learning, anomaly detection, and data generation. They are a specific kind of neural network designed to learn efficient representations of input data by compressing it into a lower-dimensional space and then reconstructing the original data from this compressed representation.
Autoencoders are trained using a reconstruction loss, which measures the difference between the original input data and the data reconstructed by the decoder. This loss encourages the autoencoder to learn a meaningful representation that can accurately reconstruct the input data. The training process involves updating the weights of the neural network to minimize this reconstruction loss.
Autoencoders come in various architectures and flavors, such as denoising autoencoders, sparse autoencoders, and variational autoencoders.
The general structure of an autoencoder consists of an encoder and a decoder…
Encoder: The encoder takes the input data and maps it to a lower-dimensional latent space representation. This process involves reducing the dimensionality of the data and capturing its most important features. The encoder's output is often referred to as the "encoding" or "code."
Decoder: The decoder takes the encoded representation from the encoder and attempts to reconstruct the original input data. It transforms the encoded representation back into the original data space. The goal is to minimize the difference between the input data and the reconstructed data.