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An autoencoder is a neural network that learns to copy its input into its output
An autoencoder can be divided into two parts: the encoder and the decoder. The encoder is a mapping from the input space into a latent space of lower dimension (bottleneck layer).
At this stage, these are just low-dimensional representations of data in an unsupervised manner
And what happens here is nothing other than a dimensionality reduction, similar to PCA.
The potential of autoencoders lies in their nonlinearity, which allows the model to learn more powerful generalizations and reconstruct the input with significantly less information loss compared to PCA.
The decoder is a mapping from the latent space with low dimension to the reconstruction space with a dimensionality equal to that of the input space
The output in reconstruction space is very similar to the input, but there is a loss of information called reconstruction error.
One possible use case of autoencoders is anomaly detection
This is more useful when we have very few negative cases and the classes are unbalanced, but can also be used in normal scenarios where labeling is difficult
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