  Generative Adversarial Networks have been employed successfully to generate
high-resolution augmented images of size 1024^2. Although the augmented images
generated are unprecedented, the training time of the model is exceptionally
high. Conventional GAN requires training of both Discriminator as well as the
Generator. In Progressive GAN, which is the current state-of-the-art GAN for
image augmentation, instead of training the GAN all at once, a new concept of
progressing growing of Discriminator and Generator simultaneously, was
proposed. Although the lower stages such as 4x4 and 8x8 train rather quickly,
the later stages consume a tremendous amount of time which could take days to
finish the model training. In our paper, we propose a novel pipeline that
combines Progressive GAN with slight modifications and Super Resolution GAN.
Super Resolution GAN up samples low-resolution images to high-resolution images
which can prove to be a useful resource to reduce the training time
exponentially.
