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Erschienen in:
Towards Explainable Artificial Intelligence Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

4. Understanding Neural Networks via Feature Visualization: A Survey

verfasst von : Anh Nguyen, Jason Yosinski, Jeff Clune

Erschienen in: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Verlag: Springer International Publishing

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Abstract

A neuroscience method to understanding the brain is to find and study the preferred stimuli that highly activate an individual cell or groups of cells. Recent advances in machine learning enable a family of methods to synthesize preferred stimuli that cause a neuron in an artificial or biological brain to fire strongly. Those methods are known as Activation Maximization (AM) [10] or Feature Visualization via Optimization. In this chapter, we (1) review existing AM techniques in the literature; (2) discuss a probabilistic interpretation for AM; and (3) review the applications of AM in debugging and explaining networks.

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Vorheriges Kapitel Interpretability in Intelligent Systems – A New Concept?
Nächstes Kapitel Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation
Fußnoten
1
In this chapter, “neuron”, “cell”, “unit”, and “feature” are used interchangeably.
 
2
Also sometimes referred to as feature visualization [29, 32, 48]. In this chapter, the phrase “visualize a unit” means “synthesize preferred images for a single neuron”.
 
3
Therefore, hereafter, we will write a(x) instead of \(a(\theta , x)\), omitting \(\theta \), for simplicity.
 
4
We abuse notation slightly in the interest of space and denote as \(N(0, \epsilon _3^2)\) a sample from that distribution. The first step size is given as \(\epsilon _{12}\) in anticipation of later splitting into separate \(\epsilon _1\) and \(\epsilon _2\) terms.
 
5
\(\epsilon _3 = 0\) because noise was not used in DGN-AM [27].
 
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Metadaten
Titel
Understanding Neural Networks via Feature Visualization: A Survey
verfasst von
Anh Nguyen
Jason Yosinski
Jeff Clune
Copyright-Jahr
2019
Buch
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Print ISBN: 978-3-030-28953-9

Electronic ISBN: 978-3-030-28954-6

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-28954-6_4

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