Deep learning's great success motivates many practitioners and students to
learn about this exciting technology. However, it is often challenging for
beginners to take their first step due to the complexity of understanding and
applying deep learning. We present CNN Explainer, an interactive visualization
tool designed for non-experts to learn and examine convolutional neural
networks (CNNs), a foundational deep learning model architecture. Our tool
addresses key challenges that novices face while learning about CNNs, which we
identify from interviews with instructors and a survey with past students. CNN
Explainer tightly integrates a model overview that summarizes a CNN's
structure, and on-demand, dynamic visual explanation views that help users
understand the underlying components of CNNs. Through smooth transitions across
levels of abstraction, our tool enables users to inspect the interplay between
low-level mathematical operations and high-level model structures. A
qualitative user study shows that CNN Explainer helps users more easily
understand the inner workings of CNNs, and is engaging and enjoyable to use. We
also derive design lessons from our study. Developed using modern web
technologies, CNN Explainer runs locally in users' web browsers without the
need for installation or specialized hardware, broadening the public's
education access to modern deep learning techniques.

CNN Explainer 是一个用于学习和检查卷积神经网络的交互式可视化工具，旨在解决初学者在学习 CNNs 时面临的关键挑战。该工具紧密集成了模型概述和动态视觉解释视图，使用户能够检查低级数学运算和高级模型结构之间的相互作用，并通过平滑的抽象级别转换。通过定性用户研究，发现 CNN Explainer 可以帮助用户更容易地了解 CNNs 的内部工作，并且使用起来引人入胜。