Fred Hohman

CNN 101: Interactive Visual Learning for Convolutional Neural Networks

Extended Abstracts on ACM Human Factors in Computing Systems (CHI), 2020

Abstract

The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology. However, it is often challenging for learners to take the first steps due to the complexity of deep learning models. We present our ongoing work, CNN 101, an interactive visualization system for explaining and teaching convolutional neural networks. Through tightly integrated interactive views, CNN 101 offers both overview and detailed descriptions of how a model works. Built using modern web technologies, CNN 101 runs locally in users’ web browsers without requiring specialized hardware, broadening the public’s education access to modern deep learning techniques.

Materials

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BibTeX

			
@inproceedings{wang2020cnn101,
  title={CNN 101: Interactive Visual Learning for Convolutional Neural Networks},
  author={Wang, Zijie J. and Turko, Robert and Shaikh, Omar and Park, Haekyu and Das, Nilaksh and Hohman, Fred and Kahng, Minsuk and Chau, Duen Horng (Polo)},
  booktitle={Proceedings of the 2020 CHI Conference Extended Abstracts on Human Factors in Computing Systems},
  publisher={ACM},
  year={2020}
}