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ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation
Fred Hohman1, Nathan Hodas2, Duen Horng Chau3
1College of Computing, Georgia Institute of Technology Atlanta, GA 30332, USA.
Abstract:
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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