We have learned several types of GANs, and the applications of them are endless. We have seen how the generator learns the distribution of real data and generates new realistic samples. We will now see a really different and very innovative type of GAN called the CycleGAN.
Unlike other GANs, the CycleGAN maps the data from one domain to another domain, which implies that here we try to learn the mapping from the distribution of images from one domain to the distribution of images in another domain. To put it simply, we translate images from one domain to another.
What does this mean? Assume we want to convert a grayscale image to a colored image. The grayscale image is one domain and the colored image is ...
