What is the main advantage of using progressive training in ProGAN?

The main advantage of using progressive training in ProGAN is that it improves the speed and stability of GAN training by starting with low-resolution images and gradually increasing the resolution.

Progressive training in ProGAN begins by training the GAN on low-resolution images, such as 4x4 pixels, and incrementally adds layers to increase the resolution throughout the training process. This approach allows the generator to first learn to produce accurate low-resolution images, which can then be refined as the resolution increases. This method avoids the challenge of immediately generating high-resolution images, which can be complex and slow to learn, thereby enhancing both the speed and stability of the training process.