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.

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DOI

			
@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},
    doi={10.1145/3027063.3053103}
}