Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as “black-boxes” due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user’s data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

Project

PDF

Video

Poster

Code

			
@inproceedings{hohman2017shapeshop,
    title={ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation},
    author={Hohman, Fred and Hodas, Nathan and Chau, Duen Horng},
    booktitle={Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems},
    year={2017},
    publisher={ACM}
}