TY - CHAP
T1 - A Rate-Distortion Framework for Explaining Black-Box Model Decisions
AU - Kolek, Stefan
AU - Nguyen, Duc Anh
AU - Levie, Ron
AU - Bruna, Joan
AU - Kutyniok, Gitta
N1 - Funding Information: Acknowledgements. G.K. acknowledges partial support by the ONE Munich Strategy Forum (LMU Munich, TU Munich, and the Bavarian Ministery for Science and Art), the German Research Foundation under Grants DFG-SPP-2298, KU 1446/31-1 and KU 1446/32-1, and the BMBF under Grant MaGriDo. R.L. acknowledges support by the DFG SPP 1798, KU 1446/21-2 “Compressed Sensing in Information Processin” through Project Massive MIMO-II. Publisher Copyright: © 2022, The Author(s). DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2022
Y1 - 2022
N2 - We present the Rate-Distortion Explanation (RDE) framework, a mathematically well-founded method for explaining black-box model decisions. The framework is based on perturbations of the target input signal and applies to any differentiable pre-trained model such as neural networks. Our experiments demonstrate the framework’s adaptability to diverse data modalities, particularly images, audio, and physical simulations of urban environments.
AB - We present the Rate-Distortion Explanation (RDE) framework, a mathematically well-founded method for explaining black-box model decisions. The framework is based on perturbations of the target input signal and applies to any differentiable pre-trained model such as neural networks. Our experiments demonstrate the framework’s adaptability to diverse data modalities, particularly images, audio, and physical simulations of urban environments.
UR - https://www.scopus.com/pages/publications/85128912840
U2 - 10.1007/978-3-031-04083-2_6
DO - 10.1007/978-3-031-04083-2_6
M3 - Chapter
SN - 9783031040825
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 91
EP - 115
BT - xxAI@ICML
A2 - Holzinger, Andreas
A2 - Goebel, Randy
A2 - Fong, Ruth
A2 - Moon, Taesup
A2 - Müller, Klaus-Robert
A2 - Samek, Wojciech
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers, xxAI 2020, held in Conjunction with ICML 2020
Y2 - 18 July 2020 through 18 July 2020
ER -