Essays about: "Data Augmentation Techniques"
Showing result 1 - 5 of 72 essays containing the words Data Augmentation Techniques.
-
1. Robust Object Recognition and Tracking with Drones
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : The Skara Skyddsängel project explores an innovative method of providing illumination for cyclists along a 20km unlit bike lane using drones. Current GNSS approach performs generally well but further improvements are need for better robustness. Consequently, this thesis project is raised to seek a robust solution in the field of computer vision. READ MORE
-
2. 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
-
3. Exploring adaptation of self-supervised representation learning to histopathology images for liver cancer detection
University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknikAbstract : This thesis explores adapting self-supervised representation learning to visual domains beyond natural scenes, focusing on medical imaging. The research addresses the central question: "How can self-supervised representation learning be specifically adapted for detecting liver cancer in histopathology images?" The study utilizes the PAIP 2019 dataset for liver cancer segmentation and employs a self-supervised approach based on the VICReg method. READ MORE
-
4. Data Augmentation: Enhancing Named Entity Recognition Performance on Swedish Medical Texts
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : Named Entity Recognition (NER) refers to the task of locating relevant information within text sequences. Within the medical domain, it can benefit applications such as de-identifying patient records or extracting valuable data for other downstream tasks. READ MORE
-
5. Data Driven Augmentation for Deep Learning Applications
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Deep learning models are achieving remarkable performance on numerous tasks across various fields and applications. However, current deep learning models often suffer from overfitting and are therefore heavily reliant on regularization techniques such as data augmentation. READ MORE
