Diffeomorphic Counterfactuals With Generative Models

Counterfactuals can explain classification decisions of neural networks in a human interpretable way. We propose a simple but effective method to generate such counterfactuals. More specifically, we perform a suitable diffeomorphic coordinate transformation and then perform gradient ascent in these...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 46; H. 5; S. 3257 - 3274
Hauptverfasser:	Dombrowski, Ann-Kathrin, Gerken, Jan E., Muller, Klaus-Robert, Kessel, Pan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.05.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Annan data- och informationsvetenskap Artificial intelligence Computational modeling Computer graphics and computer vision Coordinate transformations Counterfactual Explanations Data Manifold Data models Datorgrafik och datorseende Differential geometry Explainable Artificial Intelligence Generative Models Geometri Geometry Manifolds Neural networks Other Computer and Information Science Semantics Task analysis
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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