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. Users can interactively visualize and inspect the data transformation and flow of intermediate results in a CNN. 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 operations (e.g., mathematical computations) and high-level outcomes (e.g., class predictions). To better understand our tool's benefits, we conducted a qualitative user study, which shows that CNN Explainer can help 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。

CNN解释器：交互可视化学习卷积神经网络