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  author    = {Matthew D. Zeiler and
               Rob Fergus},
  title     = {Visualizing and Understanding Convolutional Networks},
  booktitle = {Computer Vision - {ECCV} 2014 - 13th European Conference, Zurich,
               Switzerland, September 6-12, 2014, Proceedings, Part {I}},
  series    = {Lecture Notes in Computer Science},
  volume    = {8689},
  pages     = {818--833},
  publisher = {Springer},
  year      = {2014},
  url       = {https://doi.org/10.1007/978-3-319-10590-1_53},
}


@article{bach2015pixel,
  author    = {Sebastian Bach and
               Alexander Binder and
               Gr{\'e}goire Montavon and
               Frederick Klauschen and
               Klaus-Robert M{\"u}ller and
               Wojciech Samek},
  title     = {On pixel-wise explanations for non-linear classifier decisions by
               layer-wise relevance propagation},
  journal   = {PloS one},
  volume    = {10},
  number    = {7},
  pages     = {e0130140},
  year      = {2015},
  publisher = {Public Library of Science San Francisco, CA USA},
  url       = {https://doi.org/10.1371/journal.pone.0130140}
}

@inproceedings{springenberg2015striving,
  author    = {Jost Tobias Springenberg and
               Alexey Dosovitskiy and
               Thomas Brox and
               Martin A. Riedmiller},
  title     = {Striving for Simplicity: The All Convolutional Net},
  booktitle = {3rd International Conference on Learning Representations, {ICLR} 2015,
               San Diego, CA, USA, May 7-9, 2015, Workshop Track Proceedings},
  year      = {2015},
  url       = {http://arxiv.org/abs/1412.6806},
}

@inproceedings{zhang2016top,
  author    = {Jianming Zhang and
               Zhe L. Lin and
               Jonathan Brandt and
               Xiaohui Shen and
               Stan Sclaroff},
  title     = {Top-Down Neural Attention by Excitation Backprop},
  booktitle = {Computer Vision - {ECCV} 2016 - 14th European Conference, Amsterdam,
               The Netherlands, October 11-14, 2016, Proceedings, Part {IV}},
  series    = {Lecture Notes in Computer Science},
  volume    = {9908},
  pages     = {543--559},
  publisher = {Springer},
  year      = {2016},
  url       = {https://doi.org/10.1007/978-3-319-46493-0_33},
}

@article{montavon2017explaining,
  author    = {Gr{\'{e}}goire Montavon and
               Sebastian Lapuschkin and
               Alexander Binder and
               Wojciech Samek and
               Klaus{-}Robert M{\"{u}}ller},
  title     = {Explaining nonlinear classification decisions with deep Taylor decomposition},
  journal   = {Pattern Recognit.},
  volume    = {65},
  pages     = {211--222},
  year      = {2017},
  url       = {https://doi.org/10.1016/j.patcog.2016.11.008},
}

@inproceedings{sundararajan2017axiomatic,
  author    = {Mukund Sundararajan and
               Ankur Taly and
               Qiqi Yan},
  title     = {Axiomatic Attribution for Deep Networks},
  booktitle = {Proceedings of the 34th International Conference on Machine Learning,
               {ICML} 2017, Sydney, NSW, Australia, 6-11 August 2017},
  series    = {Proceedings of Machine Learning Research},
  volume    = {70},
  pages     = {3319--3328},
  publisher = {{PMLR}},
  year      = {2017},
  url       = {http://proceedings.mlr.press/v70/sundararajan17a.html},
}

@article{smilkov2017smoothgrad,
  author    = {Daniel Smilkov and
               Nikhil Thorat and
               Been Kim and
               Fernanda B. Vi{\'{e}}gas and
               Martin Wattenberg},
  title     = {SmoothGrad: removing noise by adding noise},
  journal   = {CoRR},
  volume    = {abs/1706.03825},
  year      = {2017},
  url       = {https://arxiv.org/abs/1706.03825},
}

@article{DBLP:journals/corr/abs-1902-10178,
  author    = {Sebastian Lapuschkin and
               Stephan W{\"{a}}ldchen and
               Alexander Binder and
               Gr{\'{e}}goire Montavon and
               Wojciech Samek and
               Klaus{-}Robert M{\"{u}}ller},
  title     = {Unmasking Clever Hans Predictors and Assessing What Machines Really
               Learn},
  journal   = {CoRR},
  volume    = {abs/1902.10178},
  year      = {2019},
  url       = {http://arxiv.org/abs/1902.10178},
}

@article{lapuschkin2019unmasking,
  title     = {Unmasking Clever Hans predictors and assessing what machines really learn},
  author    = {Sebastian Lapuschkin and
               Stephan W{\"a}ldchen and
               Alexander Binder and
               Gr{\'e}goire Montavon and
               Wojciech Samek and
               Klaus-Robert M{\"u}ller},
  journal   = {Nature communications},
  volume    = {10},
  number    = {1},
  pages     = {1--8},
  year      = {2019},
  publisher = {Nature Publishing Group},
  url       = {https://doi.org/10.1038/s41467-019-08987-4}
}


@incollection{montavon2019layer,
  author    = {Gr{\'{e}}goire Montavon and
               Alexander Binder and
               Sebastian Lapuschkin and
               Wojciech Samek and
               Klaus{-}Robert M{\"{u}}ller},
  title     = {Layer-Wise Relevance Propagation: An Overview},
  booktitle = {Explainable {AI:} Interpreting, Explaining and Visualizing Deep Learning},
  series    = {Lecture Notes in Computer Science},
  volume    = {11700},
  pages     = {193--209},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-28954-6_10},
}

@inproceedings{dombrowski2019explanations,
  author    = {Ann{-}Kathrin Dombrowski and
               Maximilian Alber and
               Christopher J. Anders and
               Marcel Ackermann and
               Klaus{-}Robert M{\"{u}}ller and
               Pan Kessel},
  title     = {Explanations can be manipulated and geometry is to blame},
  booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference
               on Neural Information Processing Systems 2019, NeurIPS 2019, December
               8-14, 2019, Vancouver, BC, Canada},
  pages     = {13567--13578},
  year      = {2019},
  url       = {https://proceedings.neurips.cc/paper/2019/hash/bb836c01cdc9120a9c984c525e4b1a4a-Abstract.html},
}

@inproceedings{anders2020fairwashing,
  author    = {Christopher J. Anders and
               Plamen Pasliev and
               Ann{-}Kathrin Dombrowski and
               Klaus{-}Robert M{\"{u}}ller and
               Pan Kessel},
  title     = {Fairwashing explanations with off-manifold detergent},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning,
               {ICML} 2020, 13-18 July 2020, Virtual Event},
  series    = {Proceedings of Machine Learning Research},
  volume    = {119},
  pages     = {314--323},
  publisher = {{PMLR}},
  year      = {2020},
  url       = {http://proceedings.mlr.press/v119/anders20a.html},
}

@article{anders2021software,
  author    = {Christopher J. Anders and
               David Neumann and
               Wojciech Samek and
               Klaus{-}Robert M{\"{u}}ller and
               Sebastian Lapuschkin},
  title     = {Software for Dataset-wide {XAI:} From Local Explanations to Global
               Insights with Zennit, CoRelAy, and ViRelAy},
  journal   = {CoRR},
  volume    = {abs/2106.13200},
  year      = {2021},
  url       = {https://arxiv.org/abs/2106.13200},
}

@article{andeol2021learning,
  author    = {L{\'{e}}o And{\'{e}}ol and
               Yusei Kawakami and
               Yuichiro Wada and
               Takafumi Kanamori and
               Klaus{-}Robert M{\"{u}}ller and
               Gr{\'{e}}goire Montavon},
  title     = {Learning Domain Invariant Representations by Joint Wasserstein Distance
               Minimization},
  journal   = {CoRR},
  volume    = {abs/2106.04923},
  year      = {2021},
  url       = {https://arxiv.org/abs/2106.04923},
}
