In Depth
An autoencoder consists of two parts: an encoder that compresses input data into a lower-dimensional latent representation (bottleneck), and a decoder that reconstructs the original input from this compressed form. The network is trained to minimize the difference between input and output, forcing it to learn efficient representations that capture the most important features of the data.
The bottleneck forces the autoencoder to learn a compressed representation, discarding noise and irrelevant details while preserving essential structure. This makes autoencoders useful for dimensionality reduction, denoising (training on noisy data to reconstruct clean versions), anomaly detection (unusual inputs produce high reconstruction error), and feature learning.
Autoencoders come in many variants: denoising autoencoders (robust to input corruption), sparse autoencoders (encouraging sparse activations for more interpretable features), and variational autoencoders (which learn a probabilistic latent space suitable for generation). The autoencoder concept also underpins the latent space approach used in modern diffusion models like Stable Diffusion, where the denoising process operates in a compressed latent space rather than pixel space.