We all know that GAN can be used to augment and expand our dataset Can a GAN be used for data augmentation?. But my question is, is it effective and fast enough?
For example I have done experiment using this repo to train PGAN (Progressive Growing of GAN - state-of-the-art result).
- The training using large dataset such as CelebA and Cifar-10 took days using multi-GPU (Gtx1080 and 940) to produce desirable result (can't be distinguished as fake), and using my own dataset consists of 75.000 images (128x128) split into 3 classes also took days.
- After hours or days of training the model only generates few (less than 100) images.
Meanwhile I can do manual annotation and get more images during those days of training. So, is GAN effective and fast enough to replace image augmentation or manual annotation method? Or is there any a more effective GAN model for expanding dataset than the one that I've tried?