Chapter 13 - Advanced GANs to Manipulate Images

1. Why do we need a Pix2Pix GAN where a supervised learning algorithm like U-Net could have worked to generate images from contours?
   U-net only uses pixel-level loss during training. We needed pix2pix since there is no loss for realism when a U-net generates images.
2. Why do we need to optimize for 3 different loss functions in CycleGAN?
   Answer provided in the 7 points in CycleGAN section.
3. How do the tricks leverage in ProgressiveGAN help in building a StyleGAN?
   ProgressiveGAN helps the network to learn a few upsampling layers at a time so that when the image has to be increased in size, the networks responsible for generating current size images are optimal.
4. How do we identify latent vectors corresponding to a given custom image?
   By adjusting the randomly generated noise in such a way that the MSE loss between the generated image and the image of interest is as minimal as possible.