Generative modeling
Let's study generative modeling.
Suppose we have a function f.
We say f is a generative model if the distribution it produces, known as pushforward distribution, matches a data distribution.
But what is a pushforward distribution?
Suppose you have a simple distribution. Now, push every sample through f. The resulting distribution on the other side is the pushforward.
The goal is to learn an f whose pushforward matches real data.
So, generative modeling is a learning problem. Find the right f.
In machine learning, we can have two types of models: generative and discriminative.
Discriminative models focus on mapping individual samples to their corresponding labels, like a classification model that says if an image portrays a rainy day or a sunny day.
Generative models are about mapping from one distribution to another, like transforming an image portraying a rainy day to a sunny day.
There is a novel method termed Drifting Models that realizes that in one step.