The idea of a cycle GAN is that it has two generators and two discriminators. One generator takes in the original image, one produces some sort of fake H&E image. And we also have a discriminator that is going to try to figure out if it's a real image or a fake H & E image. So through this process, your generator is going to improve and improve and improve until you get really realistic images like they came from the original data set. This was used very frequently before 2020 for all kinds of applications. It was kind of like, I'd say the first primitive form of generative AI.

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