Understanding the notion of latent space

Recall from the previous chapter that a latent space is nothing but a compressed representation of the input data in a lower dimensional space. It essentially includes features that are crucial to the identification of the original input. To better understand this notion, it is helpful to try to mentally visualize what type of information may be encoded by the latent space. A useful analogy can be to think of how we ourselves create content, with our imagination. Suppose you were asked to create an imaginary animal. What information would you be relying on to create this creature? You will sample features from animals you have previously seen, features such as their color, or whether they are bi-pedal, quadri-pedal, a mammal or reptile, land-or sea-dwelling, and so on. As it turns out, we ourselves develop latent models of the world, as...