Is it possible to improve an image classification model with a generator (trained class conditionally). (so this is same source/target distribution and same source/target task, so not domain adaptation)
NB: "improve" can be in terms of robustness, calibration, or outright precision/recall improvement , OOD performance
e.g. I train an image classification model on data A, and then train a GAN conditionally on data A to generate an image with a class prompt. Then use the GAN to generate additional samples to train my image classification model.
NB: Its not actually an image classifier I am trying to improve but checking if the concept of using a generator to improve discriminator performance generally.