Summary

Using AI in image editing is already prevalent now, and all this started at around the time that the iGAN was introduced. We learned about the key principle of the iGAN being to first project an image onto a manifold and then directly perform editing on the manifold. We then optimize this on the latent variables and generate an edited image that is natural-looking. This is in contrast with previous methods that could only change generated images indirectly by manipulating latent variables.

GauGAN incorporates many advanced techniques to generate crisp images from semantic segmentation masks. This includes the use of hinge loss and feature matching loss. However, the key ingredient is SPADE, which provides superior performance when using a segmentation mask as input. SPADE performs normalization on a local segmentation map to preserve its semantic meaning, which helps us to produce high-quality images. So far, we have been using images with up to 256x256 resolution to train...