Marina M.-C. Höhne

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 36

Top Topics

Top Venues

Trans. Mach. Learn. Res.

Pattern Recognit.

Publications

Dilyara Bareeva, Marina M.-C. Höhne, Alexander Warnecke, Lukas Pirch, Klaus-Robert Müller, Konrad Rieck, Kirill Bykov
Manipulating Feature Visualizations with Gradient Slingshots. CoRR (2024)
Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne
Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test. CoRR (2024)
Xiaoyan Yu, Jannik Franzen, Wojciech Samek, Marina M.-C. Höhne, Dagmar Kainmueller
Model Guidance via Explanations Turns Image Classifiers into Segmentation Models. CoRR (2024)
Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina M.-C. Höhne, Kirill Bykov
CoSy: Evaluating Textual Explanations of Neurons. CoRR (2024)
Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne
A Fresh Look at Sanity Checks for Saliency Maps. CoRR (2024)
Srishti Gautam, Ahcene Boubekki, Marina M.-C. Höhne, Michael Kampffmeyer
Prototypical Self-Explainable Models Without Re-training. Trans. Mach. Learn. Res. 2024 (2024)
Pia Hanfeld, Khaled Wahba, Marina M.-C. Höhne, Michael Bussmann, Wolfgang Hönig
Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches. MRS (2023)
Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne
Labeling Neural Representations with Inverse Recognition. CoRR (2023)
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 (2023)
Kirill Bykov, Klaus-Robert Müller, Marina M.-C. Höhne
Mark My Words: Dangers of Watermarked Images in ImageNet. ECAI Workshops (1) (2023)
Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer
: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. Pattern Recognit. 136 (2023)
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 (2023)
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)
Kirill Bykov, Laura Kopf, Marina M.-C. Höhne
Finding Spurious Correlations with Function-Semantic Contrast Analysis. xAI (2) (2023)
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)
Kirill Bykov, Klaus-Robert Müller, Marina M.-C. Höhne
Mark My Words: Dangers of Watermarked Images in ImageNet. CoRR (2023)
Pia Hanfeld, Marina M.-C. Höhne, Michael Bussmann, Wolfgang Hönig
Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors. CoRR (2023)
Philine 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 (2023)
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)
Srishti Gautam, Ahcene Boubekki, Marina M.-C. Höhne, Michael C. Kampffmeyer
Prototypical Self-Explainable Models Without Re-training. CoRR (2023)
Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne
Labeling Neural Representations with Inverse Recognition. NeurIPS (2023)
Anna Hedström, Philine 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 (2023)
Pia Hanfeld, Khaled Wahba, Marina M.-C. Höhne, Michael Bussmann, Wolfgang Hönig
Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches. CoRR (2023)
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)
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 (2022)
Kirill Bykov, Mayukh Deb, Dennis Grinwald, Klaus-Robert Müller, Marina M.-C. Höhne
DORA: Exploring outlier representations in Deep Neural Networks. CoRR (2022)
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 (2022)
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 (2022)
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)
Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne
NoiseGrad - Enhancing Explanations by Introducing Stochasticity to Model Weights. AAAI (2022)
Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne
Visualizing the diversity of representations learned by Bayesian neural networks. CoRR (2022)
Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft
Explaining Bayesian Neural Networks. CoRR (2021)
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 (2021)
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 (2021)
Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne
NoiseGrad: enhancing explanations by introducing stochasticity to model weights. CoRR (2021)
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 (2020)