Architecture

Autoencoder

Définition

A neural network trained to compress data into a lower-dimensional latent space and reconstruct it. Autoencoders are used for anomaly detection, dimensionality reduction, and as components in generative models like VAEs.

Termes Connexes

Encoder-Decoder

A two-part neural network architecture where the encoder compresses an input into a latent representation and the decoder generates an output from it. Used in translation, summarisation, and speech recognition systems.

Diffusion Model

A class of generative models that learn to reverse a gradual noise-addition process to produce high-quality images, audio, or video from random noise. Diffusion models power leading image generators such as Stable Diffusion and DALL-E 3.

Unsupervised Learning

A machine learning approach that discovers structure in unlabelled data. Common techniques include clustering (grouping similar items) and dimensionality reduction. It is often used for anomaly detection and exploratory data analysis.

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