10 citations · 29 across the 5 of their papers we have counts for
8 papers
RoboGrind: Intuitive and Interactive Surface Treatment with Industrial Robots
Benjamin Alt, Florian Stöckl, Silvan Müller +10
Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboG…
Model Compression Techniques in Biometrics Applications: A Survey
Eduarda Caldeira, Pedro C. Neto, Marco Huber +2
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is h…
Improving the Effectiveness of Deep Generative Data
Ruyu Wang, Sabrina Schmedding, Marco F. Huber
Recent deep generative models (DGMs) such as generative adversarial networks (GANs) and diffusion probabilistic models (DPMs) have shown their impressive ability in generating high…
Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Development
Danilo Brajovic, Niclas Renner, Vincent Philipp Goebels +7
Despite large progress in Explainable and Safe AI, practitioners suffer from a lack of regulation and standards for AI safety. In this work we merge recent regulation efforts by th…
Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning
Xinyang Wu, Elisabeth Wedernikow, Christof Nitsche +1
In recent years, the development of Artificial Intelligence (AI) has shown tremendous potential in diverse areas. Among them, reinforcement learning (RL) has proven to be an effect…
Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation
Ruyu Wang, Sabrina Hoppe, Eduardo Monari +1
Data-hunger and data-imbalance are two major pitfalls in many deep learning approaches. For example, on highly optimized production lines, defective samples are hardly acquired whi…