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Jonker, Arnoud (author)
Due to a high contribution of human error in fatal traffic accidents, efforts in research and industry for automating vehicles steadily increased the last 3 decades. To reduce accidents with other road users, automated recognition and classification of road users is crucial. The most accurate models are large convolutional neural networks,...
master thesis 2020
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Koning, Tim (author)
Reinforcement Learning (RL) is a learning paradigm where an agent learns a task by trial and error. The agent needs to explore its environment and by simultaneously receiving rewards it learns what is appropriate behaviour.<br/>Even though it has roots in machine learning, RL is essentially different from other machine learning methods. In...
master thesis 2020
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Freiherr von der Goltz, Julian (author)
Aircraft inspections after unexpected incidents, like lightning strikes, currently require a timeconsuming and costly inspection process, due to the small size of the lightning strike damages. Mainblades Inspections is working on an automated, drone-based solution, that scans the aircraft hull with a high-resolution camera. The objective of this...
master thesis 2019
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YANG, MINGHAO (author)
One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training. It can be treated as a Network Compression problem on the architecture parameters from an overparameterized network. However, there are two issues associated with most one-shot NAS methods. First, dependencies...
master thesis 2019
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Hafner, Frank (author)
Cross-modal person re-identification is the task to re-identify a person which was sensedin a first modality, like in visible light (RGB), in a second modality, like depth. Therefore, the challenge is to sense between inputs from separate modalities, without information from both modalities at the same time step. Lately, the scientific challenge...
master thesis 2018
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