Essays about: "domain randomization"
Showing result 1 - 5 of 7 essays containing the words domain randomization.
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1. Using Synthetic Data For Object Detection on the edge in Hazardous Environments
University essay from Lunds universitet/Institutionen för reglerteknikAbstract : This thesis aims to evaluate which aspects are important when generating synthetic data with the purpose of running on a lightweight object detection model on an edge device. The task we constructed was to detect Canisters and whether they feature a protective valve called a Cap or not (called a No-Cap). READ MORE
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2. Sim2Real: Generating synthetic images from industry CAD models with domain randomization
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Deep learning methods for computer vision applications require massive visual data for model training. Although it is possible to utilize public datasets such as ImageNet, MS COCO, and CIFAR-100, it becomes problematic when there is a need for more task-specific data when new training data collection typically is needed. READ MORE
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3. Targeted Improvement of a Deep Learning Object Detector Using Synthetic Training Data
University essay from Lunds universitet/Matematisk statistikAbstract : When working with object detection, the quality and quantity of the training data is often a recurrent bottleneck. This thesis proposes a technique of incrementally improving an object detector using synthetically rendered data. READ MORE
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4. Domain Adaptation to Meet the Reality-Gap from Simulation to Reality
University essay from Linköpings universitet/DatorseendeAbstract : Being able to train machine learning models on simulated data can be of great interest in several applications, one of them being for autonomous driving of cars. The reason is that it is easier to collect large labeled datasets as well as performing reinforcement learning in simulations. READ MORE
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5. Confounder Parsing for Text Matching
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : In observational studies for policy evaluation, matching is used in service of causal inference to simulate randomization and thus reduce selection bias that might occur when treatment assignment differs systematically. This is done by balancing the distribution of confounding covariates measured before treatments. READ MORE
