Latent space

Concept often used in machine learning, particularly in the context of unsupervised learning and generative models like autoencoders and generative adversarial networks (GANs). Latent space refers to a lower-dimensional representation of data that captures the most important features in a compressed form, enabling efficient data encoding and reconstruction. This concept is valuable for applications such as image generation, data augmentation, and anomaly detection, benefiting data scientists and machine learning practitioners.