Generative models cannot do better than a well balanced and representative training dataset. I've worked on generative models in the medical space in the past.

Generative models cannot do better than a well balanced and representative training dataset. I've worked on generative models in the medical space in the past. If you are looking to use a generative model to fix class imbalance, there is always the alternative of weighting your training dataset to account for the class imbalance. Both methods are effectively doing the same thing: oversampling the underrepresented class. The issues that come with generative models are not unique to machine learning. Here's a human example: imagine you are a radiologist with 20 years of experience. Throughout your career you have specialized in MRI scans. You are able to identify a wide range of pathologies. Now one day a friend walks into your office with a brain MRI of a giraffe. They ask you to determine if the giraffe is healthy. You have _some_ prior from the tens of thousands of human scans you have analyzed. Despite that, you can't make a determination with any high degree of confidence. You simply don't have a good baseline for giraffes. Any analysis you do make is assuming that learnings from human scans extend to giraffes. Generative models are useful if we are _certain_ that training data covers essentially all possible variations. Training a model on MNIST to produce numbers/digits is trivial, capturing 99.99% of the perceptible variation in handwriting is (relatively) easy. The issue with doing this for most medical applications is twofold: 1) medical training data is difficult to acquire and much less abundant than most other data types, 2) we don't have a good measure for how much of the possible variation we have captured and how much we have missed