  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.
