Exploring autoencoders
Autoencoders occupy a unique niche in the landscape of neural network architectures, playing a pivotal role in the narrative of advanced sequential models. Essentially, an autoencoder is designed to create a network where the output mirrors its input, implying a compression of the input data into a more succinct, lower-dimensional latent representation.
The autoencoder structure can be conceptualized as a dual-phase process: the encoding phase and the decoding phase.
Consider the following diagram:
Figure 11.1: Autoencoder architecture
In this diagram we make the following assumptions:
- x corresponds to the input data
- h is the compressed form of our data
- r denotes the output, a recreation or approximation of x
We can see that the two phases are represented by f and g. Let’s look at them in more detail:
