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Segmentation-guided Domain Adaptation for Efficient Depth Completion
Complete depth information and efficient estimators have become vital ingredients in scene understanding for automated driving tasks.
On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes
Furthermore, they have to be taken into consideration when self-improving the own configuration decisions based on a feedback loop concept, e. g., known from the SASO domain or the Autonomic and Organic Computing initiatives.
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