Latent space refers to a compressed, abstract representation of data that has been transformed using techniques such as autoencoders or deep learning models. It captures the essential features or patterns of the input data in a lower-dimensional space. This helps in simplifying complex datasets. For instance, latent spaces are used in image recognition to represent variations of images with fewer variables, facilitating clustering, interpolation, or semantic searches.