cnn-explainer - Interactive Tool for Visualizing Convolutional Neural Networks

Introduction to CNN Explainer

CNN Explainer is an innovative interactive visualization system crafted to make understanding Convolutional Neural Networks (CNNs) easier for non-experts. It serves as a bridge, allowing those unfamiliar with the intricacies of CNNs to gain insights into how these complex models function.

Overview

CNN Explainer is a product of a collaborative research effort between Georgia Tech and Oregon State, developed by a team of talented researchers including Jay Wang and Robert Turko among others. The initiative is anchored in providing an accessible learning experience through interactive visual aids.

Features

Interactive Visualization: The core feature of CNN Explainer is its interactive visualization system. This tool allows users to engage with different components of CNNs, making the learning process more engaging and comprehensible.
User-Friendly Interface: Designed with non-experts in mind, CNN Explainer presents a user-friendly interface that enables users to visualize complex neural network operations without needing extensive technical knowledge.
Educational Resource: It serves as an educational resource for students, educators, or anyone interested in understanding CNNs without delving deep into technical jargon or complex mathematics.

How to Access CNN Explainer

Live Demo: To explore CNN Explainer firsthand, users can access a live demo hosted at CNN Explainer Demo.
Running Locally: For those interested in a local setup, the project repository can be cloned from GitHub. Once cloned, dependencies are installed using npm, and the program is run by executing npm run dev. Users can then visit localhost:3000 on their browser to see the tool in action.

Contribution and Support

The creation of CNN Explainer was supported by contributors and collaborators from the Georgia Tech Visualization Lab, with specific acknowledgments to individuals like Anmol Chhabria and Kaan Sancak for their invaluable feedback and support.

Academic Recognition

CNN Explainer has been recognized in academic circles, with a detailed manuscript available for those interested in the deeper technical foundations of this visualization tool. The paper titled "CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization" is published in the IEEE Transactions on Visualization and Computer Graphics.

Technical Information

Citation: For academic referencing, a BibTeX entry is provided for researchers and academics who wish to cite CNN Explainer in their work.
License: The project is freely available under the MIT License, promoting open-source collaboration and development.

Contact

For further inquiries or support related to CNN Explainer, interested parties are encouraged to contact Jay Wang or open an issue on the project's GitHub page.

CNN Explainer stands as a testament to collaboration and innovation, aimed at democratizing knowledge and understanding of CNNs through intuitive and accessible visualization techniques.