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Deep Neural Network Architectures for Detection and Segmentation of Solar Farms in Satellite Imagery
(Master thesis, 2023)In response to the urgent global call for renewable energy alternatives, as dictated by the Paris Agreement, there is an increasing need to accurately map and monitor the growth of solar farms around the world. A critical ... -
Deep Procedural Generation of 3D Objects Using Real-World Data
(Master thesis, 2023)Algoritmer for 3D-objektgjenkjenning brukes i diverse felter som virtuell virkelighet, selvkjørende biler og robotikk for å innhente nyttig informasjon i tredimensjonelle systemer som posisjoner, positurer og klasser til ... -
Deep Reinforcement Learning and Generative Adversarial Networks for Abstractive Text Summarization
(Master thesis, 2018)News articles, papers and encyclopedias, among other texts can be time-consuming to digest. Often, you are not interested in reading all the material, but only some of it. Summaries can be useful to get a grasp of what ... -
Deep Reinforcement Learning for Autonomous Vehicles in Simulated Environments
(Master thesis, 2021)Sammen med dyp læring har Reinforcement Learning (forsterkningslæring) hatt flere gjennombrudd de siste årene, noe som har økt forskningsinteressen. Kombinert med økt tilgjengelighet av realistiske og open-source bilsimulatorer ... -
Deep Reinforcement Learning for Gripper Vector Estimation
(Master thesis, 2018)The problem of gripper vector estimation, also referred to as gripper pose estimation, is the problem of constructing a vector describing the pose of the end-effector of a robotic gripper, which enables it to grasp an ... -
Deep Reinforcement Learning for International Diplomacy: Learning to Play Map Variants
(Master thesis, 2023)Brettspillet Diplomacy har fått mye oppmerksomhet de siste årene som en problemstilling for kunstig intelligens. Diplomacy har et enormt kombinatorisk aksjonsrom, en miks av samarbeid og konkurranse, forhandlinger i naturlig ... -
Deep Reinforcement Learning for Spatio-Temporal Wildlife Management
(Master thesis, 2023)De siste 50 årene har globale dyrelivsbestander opplevd betraktelig nedgang som har ført til en biologisk mangfoldskrise hvor et betydelig antall dyrearter har blitt utrydningstruet. Denne oppgaven tester ytelsen til ulike ... -
Deep Reinforcement Learning for Supporting Ambulance Dispatch Decisions
(Master thesis, 2023)Denne master avhandlingen utforsker bruken av forsterkningslæring og \acrfull{ppo} til ambulanseutsendelse-problemet, som et mulig beslutningsstøtteverktøy for Akutt medisin sentralen (AMK) i Oslo og Akershus, Oslo ... -
Deep reinforcement Learning Using Monte-Carlo Tree Search for Hex and Othello
(Master thesis, 2020)Når Deepminds AlphaGo-program slo den menneskelige profesjonelle Go-spilleren Fan Hui i 2015 var dette et stort gjennombrudd for kunstig intelligens til spilling. Go hadde vist seg å motstå de teknikkene som lenge hadde ... -
Deep Representation Learning for Personalised High Granularity Cycling Performance Prediction
(Master thesis, 2021)Abstract will be available on 2024-07-12 -
Deep Representation Learning for Personalised High Granularity Cycling Performance Prediction
(Master thesis, 2021)Abstract will be available on 2024-07-12 -
Deep Reservoir Computing Using Cellular Automata
(Master thesis, 2017)Recurrent Neural Networks (RNNs) is a prominent concept within artificial intelligence. RNNs are inspired by Biological Neural Networks (BNNs) and provide an intuitive representation of how BNNs work. Derived from the more ... -
Deep Smoke Removal from Minimally Invasive Surgery Videos
(Journal article; Peer reviewed, 2018)During video-guided minimally invasive surgery, quality of frames may be degraded severely by cauterization-induced smoke and condensation of vapor. This degradation of quality creates discomfort for the operating surgeon, ... -
Deep transfer learning for failure prediction across failure types
(Journal article; Peer reviewed, 2022) -
A deep unsupervised learning framework for the 4D CBCT artifact correction
(Peer reviewed; Journal article, 2022)Objective. Four-dimensional cone-beam computed tomography (4D CBCT) has unique advantages in moving target localization, tracking and therapeutic dose accumulation in adaptive radiotherapy. However, the severe fringe ... -
Deep Visual Domain Adaptation: - From Synthetic Data to the Real World
(Master thesis, 2018)In the field of computer vision, the increasing use of convolutional neural networks (CNN) fuels the need for more and more labeled training data. Synthetic data generated from computer graphics represent an alternative ... -
Deep-STRESS Capsule Video Endoscopy Image Enhancement
(Journal article; Peer reviewed, 2018)This paper proposes a unified framework for capsule video endoscopy image enhancement with an objective to enhance the diagnostic values of these images. The proposed method is based on a hybrid approach of deep learning ... -
DeepChanger
(Master thesis, 2020)Bruken av AI teknologi er i dagens samfunn voksende, og brukes til mange dagligdagse formål. Med denne bruken kommer også bruk av AI for ondsinned bruk, med digitale angrep med hjelp av AI, eller angrep mot AI systemer. ... -
DeepPrivacy: A GAN-based framework for image anonymization
(Master thesis, 2019)Samle data fra selvkjørende biler uten å anonymisere personlig informasjon er ulovlig etter introduksjonen av Personvernforordningen (GDPR) i 2018. For å samle data for å trene og validere maskinlæringsmodeller, må vi ... -
DeepPrivacy: A Generative Adversarial Network for Face Anonymization
(Chapter, 2019)We propose a novel architecture which is able to automatically anonymize faces in images while retaining the original data distribution. We ensure total anonymization of all faces in an image by generating images exclusively ...