Image-to-Image translation converts one image to another such as the edges of the image to the photo image. The image-to-Image translation has its applications in colorization and super-resolution. However, many of the implementation ideas specific to the pix2pix algorithm are also relevant for those studying novel image synthesis. An advanced view of the Image-to-Image translation architecture is depicted here. Similar to many image synthesis models, this uses a Conditional-GAN framework.
The generator has found its application in generating a higher-level representation of the data after the final encode layer.
As the name suggests, this module discriminates between the truly obtained image and a false image generation. The architecture used here is “PatchGAN”.
Discriminators are trained to obtain perfect results. There are different ways to train the model. The best algorithm was chosen to carry out the model training.
Evaluation is essential as it will give more information about the type of model chosen. Whether the model needs to be re-designed or not. “Inception Score” is calculated to carry out the evaluation process.