Publication

Visualizing the Hidden Activity of Artificial Neural Networks

Rauber, P. E., Fadel, S. G., Falcao, A. X. & Telea, A. C., Jan-2017, In : Ieee transactions on visualization and computer graphics. 23, 1, p. 101-110 10 p.

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DOI

  • Paulo E. Rauber
  • Samuel G. Fadel
  • Alexandre X. Falcao
  • Alexandru C. Telea

In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons. Through experiments conducted in three traditional image classification benchmark datasets, we show how visualization can provide highly valuable feedback for network designers. For instance, our discoveries in one of these datasets (SVHN) include the presence of interpretable clusters of learned representations, and the partitioning of artificial neurons into groups with apparently related discriminative roles.

Original languageEnglish
Pages (from-to)101-110
Number of pages10
JournalIeee transactions on visualization and computer graphics
Volume23
Issue number1
Publication statusPublished - Jan-2017
EventIEEE VIS Conference - Baltimore, Moldova, Republic of
Duration: 23-Oct-201628-Oct-2016

Event

IEEE VIS Conference

23/10/201628/10/2016

Baltimore, Moldova, Republic of

Event: Conference

    Keywords

  • Artificial neural networks, dimensionality reduction, algorithm understanding, DIMENSIONALITY REDUCTION, PROJECTION, EXPLORATION

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