"GANs and the variations are the most interesting idea in the last 10 years in ML." The potential of Generative Adversarial Networks (GANs) was already witnessed at the Sotheby's auction, a couple of years ago when the painting titled Edmond de Belamy, from La Famille de Belamy was sold for a whopping $432,500, and it now hangs opposite the works of pop art geniuses like Andy Warhol. The applications of GANs have made their presence felt in the esoteric trading floors, nuclear facilities and even the office of Presidents. The rise in its popularity indicates that its usage is only limited by the imagination of its users. In the era of APIs, it's a no-brainer to not to build algorithms from scratch.

Darker green means that samples in that region are more likely to be real; darker purple, more likely to be fake. As a GAN approaches the optimum, the whole heatmap will become more gray overall, signalling that the discriminator can no longer easily distinguish fake examples from the real ones. In a GAN, its two networks influence each other as they iteratively update themselves. A great use for GAN Lab is to use its visualization to learn how the generator incrementally updates to improve itself to generate fake samples that are increasingly more realistic. The generator does it by trying to fool the discriminator.

Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology. While visual and interactive approaches have been successfully developed to help people more easily learn deep learning, most existing tools focus on simpler models. In this work, we present GAN Lab, the first interactive visualization tool designed for non-experts to learn and experiment with Generative Adversarial Networks (GANs), a popular class of complex deep learning models. With GAN Lab, users can interactively train generative models and visualize the dynamic training process's intermediate results. GAN Lab tightly integrates an model overview graph that summarizes GAN's structure, and a layered distributions view that helps users interpret the interplay between submodels. GAN Lab introduces new interactive experimentation features for learning complex deep learning models, such as step-by-step training at multiple levels of abstraction for understanding intricate training dynamics. Implemented using TensorFlow.js, GAN Lab is accessible to anyone via modern web browsers, without the need for installation or specialized hardware, overcoming a major practical challenge in deploying interactive tools for deep learning.