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.

本文介绍了 GAN Lab，这是第一个面向非专家的交互式可视化工具，旨在帮助用户学习和实验复杂的深度学习模型，它紧密集成了模型概述图和分层分布图，具有与多个抽象级别的逐步训练等新型交互式实验特性。GAN Lab使用TensorFlow.js实现，可以通过现代Web浏览器访问，无需安装或专用硬件。

GAN实验室：使用交互式可视化实验了解复杂的深度生成模型