Autoencoder

An autoencoder is a neural network architecture that learns efficient data representations by training to encode input data into a compressed latent representation and then decode it back to reconstruct the original input. The bottleneck layer forces the network to learn the most salient features of the data in a lower-dimensional space, making autoencoders valuable for dimensionality reduction, feature learning, denoising, and generative applications.

The basic autoencoder consists of an encoder that maps high-dimensional input to a lower-dimensional latent code, and a decoder that reconstructs the input from this code. The network is trained to minimize reconstruction error, forcing it to learn which features are most important for faithful reconstruction. When the latent space is smaller than the input, the autoencoder must discover compact, informative representations that capture the essential structure of the data.

Variational Autoencoders (VAEs) extend the basic architecture by imposing a probabilistic structure on the latent space, typically constraining it to follow a Gaussian distribution. This enables generation of new data by sampling from the latent distribution and decoding the samples. VAEs provide a principled framework for generative modeling with smooth, interpretable latent spaces, making them useful for controlled generation and interpolation between data points.

Practical applications of autoencoders include anomaly detection (where high reconstruction error signals unusual inputs), data denoising (training on corrupted inputs with clean targets), dimensionality reduction for visualization and downstream ML tasks, and as components within larger architectures. Latent diffusion models use a VAE encoder-decoder pair to map between pixel space and a compressed latent space where the diffusion process operates more efficiently. In enterprise settings, autoencoders are used for fraud detection, sensor data compression, image quality enhancement, and learning features from unlabeled data.