In GANs, what is a "latent space"?

In the context of Generative Adversarial Networks (GANs), latent space refers to a compressed representation of the input data. This space is often created through a process of encoding, where high-dimensional data, such as images, is transformed into a lower-dimensional form. The latent space captures the essential features and characteristics of the data without maintaining all of its complexity.

The notion of a compressed representation is critical because, in GANs, the generator model samples from this latent space to produce new data instances. This allows the generator to create variations of the dataset based on the underlying patterns learned during training. By manipulating points in the latent space, one can generate diverse outputs that retain the structure of the original data while also introducing novel features.

Understanding latent space is essential for iterating and improving the quality of generated outputs, as it plays a pivotal role in how well the GAN can learn and represent the data distribution.