loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Florence Böttger 1 ; Tim Cech 2 ; Willy Scheibel 1 and Jürgen Döllner 2

Affiliations: 1 University of Potsdam, Digital Engineering Faculty, Hasso Plattner Institute, Germany ; 2 University of Potsdam, Digital Engineering Faculty, Germany

Keyword(s): Counterfactuals, Explainable Artificial Intelligence, Convolutional Neural Networks.

Abstract: As machine learning models are becoming more widespread and see use in high-stake decisions, the explainability of these decisions is getting more relevant. One approach for explainability are counterfactual explanations, which are defined as changes to a data point such that it appears as a different class. Their close connection to the original dataset aids their explainability. However, existing methods of creating counterfacual explanations often rely on other machine learning models, which adds an additional layer of opacity to the explanations. We propose additions to an established pipeline for creating visual counterfacual explanations by using an inherently explainable algorithm that does not rely on external models. Using annotated semantic part locations, we replace parts of the counterfactual creation process. We evaluate the approach on the CUB-200-2011 dataset. Our approach outperforms the previous results: we improve (1) the average number of edits by 0.1 edits, (2) th e keypoint accuracy of editing within any semantic parts of the image by an average of at least 7 percentage points, and (3) the keypoint accuracy of editing the same semantic parts by at least 17 percentage points. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.135.184.195

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Böttger, F.; Cech, T.; Scheibel, W. and Döllner, J. (2023). Visual Counterfactual Explanations Using Semantic Part Locations. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR; ISBN 978-989-758-671-2; ISSN 2184-3228, SciTePress, pages 63-74. DOI: 10.5220/0012179000003598

@conference{kdir23,
author={Florence Böttger. and Tim Cech. and Willy Scheibel. and Jürgen Döllner.},
title={Visual Counterfactual Explanations Using Semantic Part Locations},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR},
year={2023},
pages={63-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012179000003598},
isbn={978-989-758-671-2},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR
TI - Visual Counterfactual Explanations Using Semantic Part Locations
SN - 978-989-758-671-2
IS - 2184-3228
AU - Böttger, F.
AU - Cech, T.
AU - Scheibel, W.
AU - Döllner, J.
PY - 2023
SP - 63
EP - 74
DO - 10.5220/0012179000003598
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic