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Auto Encoding Explanatory Examples with Stochastic Paths

: Ojeda, César; Sánchez, Ramsés J.; Cvejoski, Kostadin; Schücker, Jannis; Bauckhage, Christian; Georgiev, Bogdan


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
ICPR 2020, 25th International Conference on Pattern Recognition. Proceedings : 10-15 January 2021, Milan, Italy, Virtual
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-7281-8809-6
ISBN: 978-1-7281-8808-9
International Conference on Pattern Recognition (ICPR) <25, 2021, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
01/S18038A; ML2R
Conference Paper
Fraunhofer IAIS ()
interpolation; semantics; decision making; stochastic processes; focusing; encoding; pattern recognition

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.