Our team

Articles in peer reviewed journals [5 Results]

• Gautam, S.; Höhne, M.; Hansen, S.; Jenssen, R.; Kampffmeyer, M. (2023): This looks More Like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. Pattern Recognition. (April): p. 109172. Online: https://doi.org/10.1016/j.patcog.2022.109172
• Bykov, K.; Müller, K.; Höhne, M. (2023): Mark My Words: Dangers of Watermarked Images in ImageNet. arXiv. : p. 1-10. Online: https://doi.org/10.48550/arXiv.2303.05498
• Bykov, K.; Deb, M.; Grinwald, D.; Müller, C.; Höhne, M. (2023): DORA: Exploring outlier representations in Deep Neural Networks. arXiv. : p. 1-34. Online: https://doi.org/10.48550/arXiv.2206.04530
• Bommer, P.; Kretschmer, M.; Hedström, A.; Bareeva, D.; Höhne, M. (2023): Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science. arXiv. : p. 1-30. Online: https://doi.org/10.48550/arXiv.2303.00652
• Hedström, A.; Bommer, P.; Wickstrøm, K.; Samek, W.; Lapuschkin, S.; Höhne, M. (2023): The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. arXiv. : p. 1-30. Online: https://doi.org/10.48550/arXiv.2302.07265

Bookchapters and proceedings [1 result]

• Bykov, K.; Kopf, L.; Höhne, M. (2023): Finding Spurious Correlations with Function-Semantic Contrast Analysis. In: Springer(eds.): Proceedings. 1st World Conference On eXplainable Artificial Intelligence (xAI 2023). Springer, Lissabon, p. 1-24.

Orals and posters [8 Results]

• Höhne, M. (2023): How much can I trust you? Towards Understanding Neural Networks.
• Höhne, M. (2023): KI - Perspektiven in der Bioökonomie.
• Bommer, P.; Kretschmer, M.; Hedström, A.; Bareeva, D.; Höhne, M. (2023): Evaluation of explainable AI solutions in climate science.
• Bykov, K.; Deb, M.; Grinwald, D.; Muller, K.; Höhne, M. (2023): DORA: Exploring outlier representations in Deep Neural Networks.
• Bykov, K.; Muller, K.; Höhne, M. (2023): Mark My Words: Dangers of Watermarked Images in ImageNet.
• Höhne, M. (2023): How much can I trust you? Towards Understanding Neural Networks.
• Bykov, K.; Kopf, L.; Höhne, M. (2023): Finding Spurious Correlations with Function-Semantic Contrast Analysis.
• Gautam, S.; Boubekki, A.; Hansen, S.; Salahuddin, S.; Jenssen, R.; Höhne, M.; Kampffmeyer, M. (2022): ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model.