Visualizing convolutional neural networks

US10192001B2 · kind B2 · utility

Assignees

• SAS INSTITUTE, INC. · US
• North Carolina State University · US

Inventors

• Samuel Paul Leeman-Munk · Cary, US
• Saratendu Sethi · Raleigh, US
• Christopher Graham Healey · Cary, US
• Shaoliang Nie · Raleigh, US
• Kalpesh Padia · Raleigh, US
• Ravinder Devarajan · Cary, US
• David James Caira · Chapel Hill, US
• Jordan Riley Benson · Ellerbe, US
• James Allen Cox · New Brighton, US
• Lawrence E. Lewis · Kirkland, US
• Mustafa Onur Kabul · Apex, US

Key dates

• Filing date: Oct 4, 2017
• Grant date: Jan 29, 2019
• Expiry date: Oct 13, 2037

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/048
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Convolutional neural networks can be visualized. For example, a graphical user interface (GUI) can include a matrix of symbols indicating feature-map values that represent a likelihood of a particular feature being present or absent in an input to a convolutional neural network. The GUI can also include a node-link diagram representing a feed forward neural network that forms part of the convolutional neural network. The node-link diagram can include a first row of symbols representing an input layer to the feed forward neural network, a second row of symbols representing a hidden layer of the feed forward neural network, and a third row of symbols representing an output layer of the feed forward neural network. Lines between the rows of symbols can represent connections between nodes in the input layer, the hidden layer, and the output layer of the feed forward neural network.