Summary
In this chapter, we explored the creative side of GAN research through the lens of image-to-image translation tasks. While the creative implications are obvious, such techniques also open up avenues to improve research and development of computer vision models for domains where datasets are hard to get.
We started off the chapter by understanding the paired image-to-image translation task. This task provides training data where source and destination domains have paired training samples. We explored this task using the pix2pix GAN architecture. Through this architecture, we explored how the encoder-decoder architecture is useful for developing generators that can produce high-fidelity outputs. The pix2pix paper took the encoder-decoder architecture one step further by making use of skip connections or a U-Net-style generator.
This setup also presented another powerful concept, called the PatchGAN discriminator, which works elegantly to assist the overall GAN with...
