In this chapter, we have learned how to turn paintings into photos using a CycleGAN. We started with an introduction to CyleGANs and explored the architectures of networks involved in CycleGANs. We also explored the different loss functions required to train CycleGANs. This was followed by an implementation of CycleGAN in the Keras framework. We trained the CycleGAN on the monet2photo dataset and visualized the generated images, the losses, and the graphs for different networks. Before concluding the chapter, we explored the real-world use cases of CycleGANs.
In the next chapter, we will work on the pix2pix network for image-to-image translation. In pix2pix, we will explore conditional GANs for image translation.
