Discriminative models will learn the boundary conditions between classes for a distribution:
- Discriminative models get their power from more data
- These models are not designed to work in an unsupervised manner or with unlabeled data
This can be described in a more graphical way, as follows:
- Generative models will model the distribution of the classes for a given input distribution:
- This creates a probabilistic model of each class in order to estimate the distribution
- A generative model has the ability to use unlabeled data since it learns labels during the training process
This can be described in a more graphical way, as follows:
So, generative models are incredibly difficult to produce as they have to accurately model and reproduce the input distribution. The discriminative models are learning decision boundaries, which is why neural networks have been incredibly successful in recent years. The GAN architecture represents a radical departure from older techniques in the generative modeling area. We'll cover how neural networks are developed and then dive right in the GAN architecture development.