Now we will see another very interesting type of autoencoders called variational autoencoders (VAE). Unlike other autoencoders, VAEs are generative models that imply they learn to generate new data just like GANs.
Let's say we have a dataset containing facial images of many individuals. When we train our variational autoencoder with this dataset, it learns to generate new realistic faces that are not seen in the dataset. VAEs have various applications because of their generative nature and some of them include generating images, songs, and so on. But what makes VAE generative and how is it different than other autoencoders? Let's learn that in the coming section.
Just as we learned when discussing GANs, for a model to be generative, it has to learn the distribution of the inputs. For instance, let's say we have a dataset that...
