What does the term "latent space" refer to in generative models?

The term "latent space" in generative models refers to a compressed representation that captures complex data patterns. In generative modeling, especially in techniques such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), the latent space serves as a lower-dimensional space where the essential features of the input data are encoded. This allows the model to learn intricate structures and relationships present in the original data while reducing dimensionality.

When a model is trained, it learns to map input data into this latent space, where similar data points are positioned close together. This representation is crucial because it allows for the generation of new data points by sampling from this space and decoding them back into the high-dimensional output space. Thus, the latent space plays a key role in the generative process by enabling the model to produce outputs that are coherent and representative of the underlying data distribution.