Latent space is the compressed, abstract representation of data learned by a neural network — a lower-dimensional space where meaningful features are encoded. In a well-trained latent space, similar inputs are close together and interpolation between points produces semantically meaningful transitions. Autoencoders and VAEs learn latent spaces explicitly; GANs and diffusion models sample from them to generate new data. Latent spaces enable powerful capabilities like finding similar images, morphing between concepts, and anomaly detection.