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ORCIDMarina M.-C. Höhne
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2020 – today
- 2024
[j7]Srishti Gautam, Ahcene Boubekki, Marina M.-C. Höhne, Michael Kampffmeyer:
Prototypical Self-Explainable Models Without Re-training. Trans. Mach. Learn. Res. 2024 (2024)- [c11]Shanghua Liu, Anna Hedström, Deepak Hanike Basavegowda, Cornelia Weltzien, Marina M.-C. Höhne:
Explainable AI in grassland monitoring: Enhancing model performance and domain adaptability. GIL 2024: 143-154 - [c10]Deepak Hanike Basavegowda, Marina M.-C. Höhne, Cornelia Weltzien:
Deep Learning-based UAV-assisted grassland monitoring to facilitate Eco-scheme 5 realization. GIL 2024: 197-202 - [c9]Xiaoyan Yu
, Jannik Franzen, Wojciech Samek, Marina M.-C. Höhne, Dagmar Kainmueller:
Model Guidance via Explanations Turns Image Classifiers into Segmentation Models. xAI (2) 2024: 113-129 - [c8]Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne:
A Fresh Look at Sanity Checks for Saliency Maps. xAI (1) 2024: 403-420 - [i23]Dilyara Bareeva, Marina M.-C. Höhne, Alexander Warnecke, Lukas Pirch, Klaus-Robert Müller, Konrad Rieck, Kirill Bykov:
Manipulating Feature Visualizations with Gradient Slingshots. CoRR abs/2401.06122 (2024) - [i22]Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne:
Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test. CoRR abs/2401.06465 (2024) - [i21]Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne:
A Fresh Look at Sanity Checks for Saliency Maps. CoRR abs/2405.02383 (2024) - [i20]Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina M.-C. Höhne, Kirill Bykov:
CoSy: Evaluating Textual Explanations of Neurons. CoRR abs/2405.20331 (2024) - [i19]Xiaoyan Yu, Jannik Franzen, Wojciech Samek, Marina M.-C. Höhne, Dagmar Kainmueller:
Model Guidance via Explanations Turns Image Classifiers into Segmentation Models. CoRR abs/2407.03009 (2024) - 2023
- [j6]Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond. J. Mach. Learn. Res. 24: 34:1-34:11 (2023) - [j5]Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer:
This looks More Like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. Pattern Recognit. 136: 109172 (2023) - [j4]Kirill Bykov, Mayukh Deb, Dennis Grinwald, Klaus-Robert Müller, Marina M.-C. Höhne:
DORA: Exploring Outlier Representations in Deep Neural Networks. Trans. Mach. Learn. Res. 2023 (2023) - [j3]Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne:
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks. Trans. Mach. Learn. Res. 2023 (2023) - [j2]Anna Hedström, Philine Lou Bommer, Kristoffer Knutsen Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. Trans. Mach. Learn. Res. 2023 (2023) - [c7]Kirill Bykov, Klaus-Robert Müller, Marina M.-C. Höhne:
Mark My Words: Dangers of Watermarked Images in ImageNet. ECAI Workshops (1) 2023: 426-434 - [c6]Pia Hanfeld, Khaled Wahba, Marina M.-C. Höhne, Michael Bussmann, Wolfgang Hönig:
Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches. MRS 2023: 78-84 - [c5]Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne:
Labeling Neural Representations with Inverse Recognition. NeurIPS 2023 - [c4]Kirill Bykov, Laura Kopf, Marina M.-C. Höhne:
Finding Spurious Correlations with Function-Semantic Contrast Analysis. xAI (2) 2023: 549-572 - [i18]Anna Hedström, Philine Lou Bommer, Kristoffer K. Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. CoRR abs/2302.07265 (2023) - [i17]Philine Lou Bommer, Marlene Kretschmer, Anna Hedström, Dilyara Bareeva, Marina M.-C. Höhne:
Finding the right XAI method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science. CoRR abs/2303.00652 (2023) - [i16]Kirill Bykov, Klaus-Robert Müller, Marina M.-C. Höhne:
Mark My Words: Dangers of Watermarked Images in ImageNet. CoRR abs/2303.05498 (2023) - [i15]Pia Hanfeld, Marina M.-C. Höhne, Michael Bussmann, Wolfgang Hönig:
Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors. CoRR abs/2305.12859 (2023) - [i14]Pia Hanfeld, Khaled Wahba, Marina M.-C. Höhne, Michael Bussmann, Wolfgang Hönig:
Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches. CoRR abs/2308.00344 (2023) - [i13]Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne:
Labeling Neural Representations with Inverse Recognition. CoRR abs/2311.13594 (2023) - [i12]Srishti Gautam, Ahcene Boubekki, Marina M.-C. Höhne, Michael C. Kampffmeyer:
Prototypical Self-Explainable Models Without Re-training. CoRR abs/2312.07822 (2023) - [i11]Shanghua Liu, Anna Hedström, Deepak Hanike Basavegowda, Cornelia Weltzien, Marina M.-C. Höhne:
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability. CoRR abs/2312.08408 (2023) - 2022
- [c3]Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne:
NoiseGrad - Enhancing Explanations by Introducing Stochasticity to Model Weights. AAAI 2022: 6132-6140 - [c2]Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer:
Demonstrating the Risk of Imbalanced Datasets in Chest X-Ray Image-Based Diagnostics by Prototypical Relevance Propagation. ISBI 2022: 1-5 - [c1]Srishti Gautam, Ahcène Boubekki, Stine Hansen, Suaiba Amina Salahuddin, Robert Jenssen, Marina M.-C. Höhne, Michael Kampffmeyer:
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model. NeurIPS 2022 - [i10]Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer:
Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation. CoRR abs/2201.03559 (2022) - [i9]Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne:
Visualizing the diversity of representations learned by Bayesian neural networks. CoRR abs/2201.10859 (2022) - [i8]Anna Hedström, Leander Weber, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations. CoRR abs/2202.06861 (2022) - [i7]Kirill Bykov, Mayukh Deb, Dennis Grinwald, Klaus-Robert Müller, Marina M.-C. Höhne:
DORA: Exploring outlier representations in Deep Neural Networks. CoRR abs/2206.04530 (2022) - [i6]Srishti Gautam, Ahcene Boubekki, Stine Hansen, Suaiba Amina Salahuddin, Robert Jenssen, Marina M.-C. Höhne, Michael Kampffmeyer:
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model. CoRR abs/2210.08151 (2022) - 2021
- [j1]Marina Marie-Claire Höhne:
Nachvollziehbare Künstliche Intelligenz: Methoden, Chancen und Risiken. Datenschutz und Datensicherheit 45(7): 453-456 (2021) - [i5]Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne:
NoiseGrad: enhancing explanations by introducing stochasticity to model weights. CoRR abs/2106.10185 (2021) - [i4]Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft:
Explaining Bayesian Neural Networks. CoRR abs/2108.10346 (2021) - [i3]Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer:
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. CoRR abs/2108.12204 (2021) - [i2]Yamen Ali, Aiham Taleb, Marina M.-C. Höhne, Christoph Lippert:
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout. CoRR abs/2109.14288 (2021) - 2020
- [i1]Kirill Bykov, Marina M.-C. Höhne, Klaus-Robert Müller, Shinichi Nakajima, Marius Kloft:
How Much Can I Trust You? - Quantifying Uncertainties in Explaining Neural Networks. CoRR abs/2006.09000 (2020)
Coauthor Index
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