CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization

Zijie Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau

External link (DOI)
View presentation:2020-10-28T18:45:00ZGMT-0600Change your timezone on the schedule page
2020-10-28T18:45:00Z
Exemplar figure
With CNN Explainer, learners can visually examine how Convolutional Neural Networks (CNNs) transform input images into classification predictions (e.g., predicting espresso for an image of a coffee cup), and interactively learn about their underlying mathematical operations. In this example, a learner uses CNN Explainer to understand how convolutional layers work through three tightly integrated views, each explaining the convolutional process in increasing levels of detail.
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Direct link to video on YouTube: https://youtu.be/SlEvmkS4Rs4

Keywords

Deep learning, machine learning, convolutional neural networks, visual analytics

Abstract

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.