An encoder squeezes the input through a narrow bottleneck and a decoder rebuilds it. Because the bottleneck cannot carry everything, the network must learn what matters.
Useful for denoising, for anomaly detection — anything reconstructed badly is unlike the training data — and, in its variational form, as a generative model in its own right.
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