14 citations · 21 across the 6 of their papers we have counts for
6 papers
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework
Franziska Schwaiger, Andrea Matic, Karsten Roscher +1
The ability to detect learned objects regardless of their appearance is crucial for autonomous systems in real-world applications. Especially for detecting humans, which is often a…
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection
Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz +2
As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated. A key concern in this regard is the ability to identify whether a gi…
Towards Human-Interpretable Prototypes for Visual Assessment of Image Classification Models
Poulami Sinhamahapatra, Lena Heidemann, Maureen Monnet +1
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…
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space
Poulami Sinhamahapatra, Rajat Koner, Karsten Roscher +1
It is essential for safety-critical applications of deep neural networks to determine when new inputs are significantly different from the training distribution. In this paper, we…
OODformer: Out-Of-Distribution Detection Transformer
Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher +2
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 train…
From Black-box to White-box: Examining Confidence Calibration under different Conditions
Franziska Schwaiger, Maximilian Henne, Fabian Küppers +3
Confidence calibration is a major concern when applying artificial neural networks in safety-critical applications. Since most research in this area has focused on classification i…