I'm beginning to study and implement GAN to generate more dataset. I'll just try to experiment with state-of-the-art GAN models as described in here https://paperswithcode.com/sota/image-generation-on-cifar-10.

The problem is I don't have 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 bigger dataset than usual image classification. I couldn't find any detailed guideline of how to prepare dataset 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.

  • $\begingroup$ Have the same question. Did u solve it now? Thanks $\endgroup$ – Tao Chen Jul 30 '20 at 2:20
  • $\begingroup$ @TaoChen I trained it using 1500 images only, there's no specific guideline for that I think $\endgroup$ – gameon67 Jul 30 '20 at 7:59

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