13 citations · 44 across the 12 of their papers we have counts for
12 papers
Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI
Ashish Rana, Ammar Shaker, Sascha Saralajew +6
Despite recent advances in machine learning and explainable AI, a gap remains in personalized preventive healthcare: predictions, interventions, and recommendations should be both…
A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations
Sascha Saralajew, Ashish Rana, Thomas Villmann +1
Prototype-based classification learning methods are known to be inherently interpretable. However, this paradigm suffers from major limitations compared to deep models, such as low…
Aligning Generalisation Between Humans and Machines
Filip Ilievski, Barbara Hammer, Frank van Harmelen +22
Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and forming decisions, but may also disrupt…
Robust Text Classification: Analyzing Prototype-Based Networks
Zhivar Sourati, Darshan Deshpande, Filip Ilievski +2
Downstream applications often require text classification models to be accurate and robust. While the accuracy of the state-of-the-art Language Models (LMs) approximates human perf…
A Human-Centric Assessment Framework for AI
Sascha Saralajew, Ammar Shaker, Zhao Xu +5
With the rise of AI systems in real-world applications comes the need for reliable and trustworthy AI. An essential aspect of this are explainable AI systems. However, there is no…
Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Night
Lukas Ewecker, Lars Ohnemus, Robin Schwager +3
Provident detection of other road users at night has the potential for increasing road safety. For this purpose, humans intuitively use visual cues, such as light cones and light r…
Provident Vehicle Detection at Night for Advanced Driver Assistance Systems
Lukas Ewecker, Ebubekir Asan, Lars Ohnemus +1
In recent years, computer vision algorithms have become more powerful. However, current algorithms mainly share one limitation: They rely on directly visible objects. This is a sig…
A Dataset for Provident Vehicle Detection at Night
Sascha Saralajew, Lars Ohnemus, Lukas Ewecker +3
In current object detection, algorithms require the object to be directly visible in order to be detected. As humans, however, we intuitively use visual cues caused by the respecti…
Provident Vehicle Detection at Night: The PVDN Dataset
Lars Ohnemus, Lukas Ewecker, Ebubekir Asan +5
For advanced driver assistance systems, it is crucial to have information about oncoming vehicles as early as possible. At night, this task is especially difficult due to poor ligh…
Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets
Simon T. Isele, Marcel P. Schilling, Fabian E. Klein +2
Research on localization and perception for Autonomous Driving is mainly focused on camera and LiDAR datasets, rarely on radar data. Manually labeling sparse radar point clouds is…
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks
Sascha Saralajew, Lars Holdijk, Maike Rees +1
Adversarial attacks and the development of (deep) neural networks robust against them are currently two widely researched topics. The robustness of Learning Vector Quantization (LV…
Prototype-based Neural Network Layers: Incorporating Vector Quantization
Sascha Saralajew, Lars Holdijk, Maike Rees +1
Neural networks currently dominate the machine learning community and they do so for good reasons. Their accuracy on complex tasks such as image classification is unrivaled at the…