What are autoencoders?
Autoencoders are a type of unsupervised neural network (i.e., no class labels or labeled data) that seek to:

Accept an input set of data (i.e., the input).
Internally compress the input data into a latent-space representation (i.e., a single vector that compresses and quantifies the input).
Reconstruct the input data from this latent representation (i.e., the output).
Typically, we think of an autoencoder as having two components/subnetworks:

Encoder: Acceptsaccepts the input data and compresses it into the latent-space. If we denote our input data as x and the encoder as E, then the output latent-space representation, s, would be s = E(x).
Decoder: The decoder is responsible for accepting the latent-space representation s, and then reconstructing the original input. If we denote the decoder function as D and the output of the detector as o, then we can represent the decoder as o = D(s).