no code implementations • 11 Apr 2024 • Poulami Sinhamahapatra, Franziska Schwaiger, Shirsha Bose, Huiyu Wang, Karsten Roscher, Stephan Guennemann
It is an inference-based method that does not require training on the domain dataset and relies on extracting relevant features from self-supervised pre-trained models.
no code implementations • 3 Apr 2024 • Florian Geissler, Karsten Roscher, Mario Trapp
Generative AI is increasingly important in software engineering, including safety engineering, where its use ensures that software does not cause harm to people.
no code implementations • 3 Apr 2024 • Poulami Sinhamahapatra, Suprosanna Shit, Anjany Sekuboyina, Malek Husseini, David Schinz, Nicolas Lenhart, Joern Menze, Jan Kirschke, Karsten Roscher, Stephan Guennemann
In this work, we propose a novel interpretable-by-design method, ProtoVerse, to find relevant sub-parts of vertebral fractures (prototypes) that reliably explain the model's decision in a human-understandable way.
1 code implementation • 10 Jul 2023 • Franziska Schwaiger, Andrea Matic, Karsten Roscher, Stephan Günnemann
The ability to detect learned objects regardless of their appearance is crucial for autonomous systems in real-world applications.
1 code implementation • 27 Mar 2023 • Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Guennemann
As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated.
no code implementations • 22 Nov 2022 • Poulami Sinhamahapatra, Lena Heidemann, Maureen Monnet, Karsten Roscher
Explaining black-box Artificial Intelligence (AI) models is a cornerstone for trustworthy AI and a prerequisite for its use in safety critical applications such that AI models can reliably assist humans in critical decisions.
no code implementations • 16 Mar 2022 • Poulami Sinhamahapatra, Rajat Koner, Karsten Roscher, Stephan Günnemann
It is essential for safety-critical applications of deep neural networks to determine when new inputs are significantly different from the training distribution.
1 code implementation • 19 Jul 2021 • Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher, Stephan Günnemann, Volker Tresp
A serious problem in image classification is that a trained model might perform well for input data that originates from the same distribution as the data available for model training, but performs much worse for out-of-distribution (OOD) samples.
no code implementations • 8 Jan 2021 • Franziska Schwaiger, Maximilian Henne, Fabian Küppers, Felippe Schmoeller Roza, Karsten Roscher, Anselm Haselhoff
Based on previous work, we study the miscalibration of object detection models with respect to image location and box scale.