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Conference Papers Year : 2017

Using prototypes to improve convolutional networks interpretability

Abstract

We propose a method that allows the interpretation of the data representation obtained by CNN, through introducing prototypes in the feature space, that are later classified into a certain category. This way we can see how the feature space is structured in link with the categories and the related task.

Domains

Neuroscience
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Dates and versions

hal-01651964 , version 1 (29-11-2017)

Identifiers

  • HAL Id : hal-01651964 , version 1

Cite

Thalita F Drumond, Thierry Viéville, Frédéric Alexandre. Using prototypes to improve convolutional networks interpretability. NIPS 2017 - 31st Annual Conference on Neural Information Processing Systems: Transparent and interpretable machine learning in safety critical environments Workshop, Dec 2017, Long Beach, United States. ⟨hal-01651964⟩
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