In this section, we covered a variety of generative models that learn the distribution of true data and try to generate data that is indistinguishable from it. We started with a simple autoencoder and built on it to understand a variant of it that uses variational inference to generate data similar to the input. We then went on to learn about GANs, which pit two models—a discriminator and a generator—against each other in a game so that the generator tries to learn to create data that looks real enough to fool the discriminator into thinking it is real.
Finally, we learned about flow-based networks, which approximate a complex probability density using a simpler one by applying several invertible transformations on it. These models are used in a variety of tasks, including—but not limited to—synthetic data generation to overcome data limitations...
