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Title: ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation

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.

Authors:
; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1361010
Report Number(s):
PNNL-SA-126087
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI EA 2017), May 6-11, 2017, Denver, Colorado, 1694-1699
Country of Publication:
United States
Language:
English

Citation Formats

Hohman, Frederick M., Hodas, Nathan O., and Chau, Duen Horng. ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation. United States: N. p., 2017. Web. doi:10.1145/3027063.3053103.
Hohman, Frederick M., Hodas, Nathan O., & Chau, Duen Horng. ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation. United States. doi:10.1145/3027063.3053103.
Hohman, Frederick M., Hodas, Nathan O., and Chau, Duen Horng. Tue . "ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation". United States. doi:10.1145/3027063.3053103.
@article{osti_1361010,
title = {ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation},
author = {Hohman, Frederick M. and Hodas, Nathan O. and Chau, Duen Horng},
abstractNote = {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.},
doi = {10.1145/3027063.3053103},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue May 30 00:00:00 EDT 2017},
month = {Tue May 30 00:00:00 EDT 2017}
}

Conference:
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