Pix2pix

For image-to-image translation tasks, pix2pix also shown impressive results. Be it converting night images to day images or vice versa, colorizing black and white images, translating sketches to photos and many more, Pix2pix has excelled in all these use-cases. The pix2pix network was introduced by Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros in their paper titled “Image-to-Image Translation with Conditional Adversarial Networks”, which is available at the following link: https://arxiv.org/abs/1611.07004.

This was an interactive demo, capable of generating real images from sketches.

Age-cGAN (Age Conditional Generative Adversarial Networks)

Face aging has many industry use cases, including cross-age face recognition, finding lost children, and in entertainment. Face aging with Conditional GANs was proposed by Grigory Antipov, Moez Baccouche, and Jean-Luc Dugelay in their paper titled Face Aging with Conditional Generative Adversarial Networks, which is available at the following link: https://arxiv.org/pdf/1702.01983.pdf.

This image shows how Age-cGAN translated face from the source age to the target age.

These were some widely popular GAN architectures. Besides these, there are thousands of GAN architectures. It depends on your requirements which architecture will suit your need.

The Rise

As Famous Theoretical Physicist Richard Feynman says:

“What I can’t create, I don’t understand”

The idea behind GANs was to train networks which understand the data. GANs now started to understand the data, with this understanding they started to create realistic looking images. Let’s witness the rise of GANs.

Edmond de Belamy

Edmond de Belamy, a painting created by Generative Adversarial Networks was sold for a staggering amount of $432, 500 at Christie’s auction. It was a big step in the progress of GANs. For the first time, the whole world witnessed GANs and their potential. Before this, GANs were mostly confined in research labs and used by machine learning engineers. This act became an entry of GANs to the general public.

This Person Does Not Exist

You may be familiar with the website https://thispersondoesnotexist.com. Last month, this was all over the Internet. The website, https://thispersondoesnotexist.com created by Philip Wan, who is a software engineer at Uber. He created this website based on the code released by NVIDIA titled StyleGAN. Every time you hit refresh, it generates a new fake face, which looks surprisingly real unable to tell whether it is fake or not. This is scary AF but disruptive at the same time. This technology has the potential to create endless virtual worlds.

Isn’t it amazing!

Deep Fakes

DeepFakes is another scary AF but disruptive technology. Based on GANs, this can paste people’s faces onto a target person in videos. DeepFakes was all over the Internet too. People speculated the downsides of this technology. But for AI researchers, this was a major breakthrough. This technology has the potential to save millions of dollars in the film industry where hours of editing required to change stuntman’s face with actors face.

This technology will always be scary, but it is up to us to use it for social good.

The Trend

StyleGAN currently is the sixth most trending python project on GitHub. The number of named GANs till now proposed are in thousands. This repository has a list of popular GANs and their respective papers https://github.com/hindupuravinash/the-gan-zoo

In Real-world

GANs have been used to enhance the graphics of games. I am super excited about this use-case of GANs. Recently, NVIDIA released a video, in which, it showed how GANs are used to gamify the environment in the video.

Conclusion

In this article, we have seen how GANs rose to fame and became a global phenomenon. I hope, we see the democratization of GANs in the coming years. In this article, we started with the birth of GANs. Then, we explored some widely popular GAN architectures. Finally, we witnessed the rise of GANs. When I see negative press around GANs, I am baffled. I believe, it is our responsibility to make everyone aware of the repercussions of GANs and how can we ethically and morally use GANs for our best. Let’s all come together and spread positivity around GANs. GANs have so much potential to create new industries and jobs. We just have to make sure that it doesn’t go into wrong hands.

Thank you for reading. If you want to get into contact, you can reach out to me at ahikailash1@gmail.com Note: Recently, I published a book on GANs titled “Generative Adversarial Networks Projects”, in which I covered most of the widely popular GAN architectures and their implementations. DCGAN, StackGAN, CycleGAN, Pix2pix, Age-cGAN, and 3D-GAN have been covered in details at the implementation level. Each architecture has a chapter dedicated to it. I have explained these networks in a very simple and descriptive language using Keras framework with Tensorflow backend. If you are working on GANs or planning to use GANs, give it a read and share your valuable feedback with me at ahikailash1@gmail.com