I'm beginning to study and implement GAN to generate more datasets. I'll just try to experiment with state-of-the-art GAN models as described here https://paperswithcode.com/sota/image-generation-on-cifar-10.
The problem is I don't have a big dataset (around 1.000) for image classification, I have tried to train and test my dataset with GoogleNet and InceptionV3 and the results are mediocre. I'm afraid that GAN will require a bigger dataset than the usual image classification. I couldn't find any detailed guideline of how to prepare datasets properly for GAN (e.g. minimum images).
So, how many images are required to produce a good GAN model?
Also, I'm curious whether if I can use my image classification dataset directly to train GAN.