Summary

In this book, my intention was to give you a taste of GANs and their applications in the world. The only limit is your imagination. There is an enormous list of different GAN architectures available, and they are becoming increasingly mature. GANs still have a fair way to go, because they still have problems, such as training instability and mode collapse, but various solutions have now been proposed, including label smoothing, instance normalization, and mini-batch discrimination. I hope that this book has helped you in the implementation of GANs for your own purposes. If you have any queries, drop me an email at [e-mail].