An Interactive Node-Link Visualization of Convolutional Neural Networks

Adam W. Harley.

Abstract

Convolutional neural networks are at the core of state-of-the-art approaches to a variety of computer vision tasks. Visualizations of neural networks typically take the form of static node-link diagrams, which illustrate only the structure of a network, rather than the behavior. Motivated by this observation, this paper presents a new interactive visualization of neural networks trained on handwritten digit recognition, with the intent of showing the actual behavior of the network given user-provided input. The user can interact with the network through a drawing pad, and watch the activation patterns of the network respond in real time.

Paper

Citation

A. W. Harley, "An Interactive Node-Link Visualization of Convolutional Neural Networks," in ISVC, pages 867-877, 2015

Bibtex format:

@inproceedings{harley2015isvc,

title = {An Interactive Node-Link Visualization of Convolutional Neural Networks},

author = {Adam W Harley},

booktitle = {ISVC},

pages = {867--877},

year = {2015}

}

Demo

Details

The networks were trained on an augmented version of MNIST, so they excel at categorizing centred upright numbers. The networks were trained in a custom neural network implementation in MATLAB; the math for the visualizations was written in Javascript; the visualization was created in WebGL. The source code for both visualizations is available here.