Autoencoder
A network that learns to squeeze data down and rebuild it, keeping only what matters.
Think of it like
Like zipping a file and unzipping it — if the copy still looks right, the middle captured the essence.
Example
A denoising autoencoder can take a grainy scan, compress it, and reconstruct a clean version.
How it actually works
An encoder shrinks the input into a small “bottleneck” representation, and a decoder tries to reconstruct the original from it. Because the bottleneck is smaller than the input, the network is forced to keep the important structure and drop the noise. It’s used for compression, denoising, and learning useful representations — the ancestor of the VAE.
For product teams
A simple building block for compression, cleanup, and feature learning.
For engineers
An encoder–decoder trained to minimize reconstruction error through a low-dimensional bottleneck.
Related
- VAE — The probabilistic, generative descendant.
- Representation — What the bottleneck produces.
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