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05 May 2021
Conference Paper
Titel

Auto Encoding Explanatory Examples with Stochastic Paths

Abstract
In this paper we ask for the main factors that determine a classifiers decision making process and uncover such factors by studying latent codes produced by auto-encoding frameworks. To deliver an explanation of a classifiers behaviour, we propose a method that provides series of examples highlighting semantic differences between the classifiers decisions. These examples are generated through interpolations in latent space. We introduce and formalize the notion of a semantic stochastic path, as a suitable stochastic process defined in feature (data) space via latent code interpolations. We then introduce the concept of semantic Lagrangians as a way to incorporate the desired classifiers behaviour and find that the solution of the associated variational problem allows for highli ghting differences in the classifier decision. Very importantly, within our framework the classifier is used as a black-box, and only its evaluation is required.
Author(s)
Ojeda, César
TU Berlin
Sánchez, Ramsés J.
Uni Bonn
Cvejoski, Kostadin
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Schücker, Jannis
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Bauckhage, Christian
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Georgiev, Bogdan
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Hauptwerk
ICPR 2020, 25th International Conference on Pattern Recognition. Proceedings
Project(s)
ML2R
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Konferenz
International Conference on Pattern Recognition (ICPR) 2021
Thumbnail Image
DOI
10.1109/ICPR48806.2021.9413267
Language
English
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Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Tags
  • interpolation

  • semantics

  • decision making

  • stochastic processes

  • focusing

  • encoding

  • pattern recognition

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