Searched for: subject:"Generative%5C+Adversarial%5C+Networks"
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document
Ueberschär, Frederik (author)
Our world climate is an incredibly complex and ever-changing system. The reality of climate change and the consequential pressure to act fast to reduce greenhouse gas emissions requires immediate attention. And yet, despite this unprecedented urgency, active public engagement needed to motivate meaningful change is still missing.<br/> <br/> This...
master thesis 2021
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Dainelli, Filippo (author)
Extra-Tropical Cyclones (ETCs) are major storm system ruling and influencing the atmospheric structure at mid-latitudes. These events are usually characterized by strong winds and heavy precipitation and cause considerable storm surges with threatening wave systems for coastal regions. The possibility to simulate these storms or to increase the...
master thesis 2020
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van Rhijn, J. (author)
Generative adversarial networks (GANs) have shown promising results when applied on partial differential equations and financial time series generation. This thesis investigates if GANs can be used to provide a strong approximation to the solution of stochastic differential equations (SDEs) of the Ito type. Standard GANs are only able to...
master thesis 2020
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Numan, Nels (author)
Virtual reality (VR) creates an exceptional experience in which users can explore virtual environments. Wearing a head-mounted display (HMD), users are able to observe a virtual world that is rendered based on their physical movement and actions. A common solution for capturing the visual and geometric information needed for the construction of...
master thesis 2020
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El Haji, Khalid (author), Posner, Noah (author), Ilbaş, Hakan (author), Karpuz, Sergen (author), Wernet, Victor (author)
As the population increases so does the waste that is generated. Manually recycling waste is expensive and slow. Computer Vision (CV) solutions aim to make this less expensive and faster. Lots of data of this waste (thousands of images) is needed to train these CV solutions. This project, called Synthetic Waste Generator (SWaG) can create...
bachelor thesis 2020
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De Meer Pardo, Fernando (author)
The scarcity of historical financial data has been a huge hindrance for the development algorithmic trading models ever since the first models were devised. Most financial models assume as hypothesis a series of characteristics regarding the nature of financial time series and seek extracting information about the state of the market through...
master thesis 2019
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Slangewal, Bart (author)
Since their conception in 2014, a large number of Generative Adversarial Networks (GANs) [2] has been pro- posed and developed. GANs have achieved great results in realistic image generation, among other fields. Recently, stunning images have been produced. The theory and application of GANs has received much attention. However, the evaluation...
bachelor thesis 2019
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Uittenbogaard, Ries (author)
In this thesis, a pipeline is created consisting of two parts. In the first part, the moving objects (cars, cyclists, pedestrians) are detected in street-view imagery using image segmentation neural networks and a LIDAR-based moving object detection approach. In the second part, those moving objects are deleted from the image data and an image...
master thesis 2018
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Hao, Haidong (author)
Percutaneous coronary intervention is a minimally-invasive procedure to treat coronary artery disease. In such procedures, X-ray angiography, a real-time imaging technique, is commonly used for image guidance to identify lesion sites and navigate catheters and guide-wires within coronary arteries. Due to the physical nature of X-ray imaging,...
master thesis 2018
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Maton, Max (author)
Creating big datasets is often difficult or expensive which causes people to augment their dataset with rendered images. This often fails to significantly improve accuracy due to a difference in distribution between real and rendered datasets. This paper shows that the gap between synthetic and real-world image distributions can be closed by...
bachelor thesis 2018
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Li, Yadong (author)
Generative adversarial networks (GANs) are a class of generative models, for which the goal is to learn from training data and then to generate data with similar characteristics. Despite the wide use of GANs, a quantitative evaluation method of their performance is lacking. In the current work, we invented a series of artificial datasets,...
master thesis 2018
Searched for: subject:"Generative%5C+Adversarial%5C+Networks"
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