Progressive GAN

GANs are powerful systems to generate high-quality samples, examples of which we have seen in the previous sections. Different works have utilized this adversarial setup to generate samples from different distributions like CIFAR10, celeb_a, LSUN-bedrooms, and so on (we covered examples using MNIST for explanation purposes). There have been some works that focused on generating higher-resolution output samples, like Lap-GANs, but they lacked perceived output quality and presented a larger set of challenges for training. Progressive GANs or Pro-GANs or PG-GANs were presented by Karras et al. in their work titled GANs for Improved Quality, Stability, and Variation¹⁴ at ICLR-2018, as a highly effective method for generating high-quality samples.

The method presented in this work not only mitigated many of the challenges present in earlier works but also brought about a very simple solution to crack this problem of generating high-quality output samples. The paper...