ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation.

Fred Hohman, Nathan Hodas, Duen Horng Chau
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引用次数: 23

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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Abstract Image

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ShapeShop:通过互动实验来理解深度学习表征。
深度学习是许多新技术背后的驱动力;然而,由于其内部复杂性难以理解,深度神经网络通常被视为“黑盒子”。很少有研究专注于帮助人们探索和理解用户数据与深度学习模型中学习表征之间的关系。我们介绍了我们正在进行的工作,ShapeShop,一个用于可视化和理解神经网络模型所学语义的交互式系统。ShapeShop使用标准的web技术构建,允许用户试验和比较深度学习模型,以帮助探索图像分类器的鲁棒性。
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