CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
| Autor: | Zijie J. Wang, Nilaksh Das, Haekyu Park, Omar Shaikh, Minsuk Kahng, Fred Hohman, Robert Turko, Duen Horng Polo Chau |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Human-Computer Interaction Computer Science - Computer Vision and Pattern Recognition Convolutional neural network Human-Computer Interaction (cs.HC) Machine Learning (cs.LG) Human–computer interaction Computer Graphics Humans Interactive visualization Abstraction (linguistics) Structure (mathematical logic) business.industry Deep learning Computer Graphics and Computer-Aided Design Visualization Artificial Intelligence (cs.AI) Face (geometry) Signal Processing Key (cryptography) Computer Vision and Pattern Recognition Artificial intelligence Neural Networks Computer business Software |
| Zdroj: | IEEE transactions on visualization and computer graphics. 27(2) |
| ISSN: | 1941-0506 |
| Popis: | 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. Comment: 11 pages, 14 figures, to be presented at IEEE VIS 2020. For a demo video, see https://youtu.be/HnWIHWFbuUQ . For a live demo, visit https://poloclub.github.io/cnn-explainer/ |
| Databáze: | OpenAIRE |
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