Sökning: "target domain"

Visar resultat 16 - 20 av 182 uppsatser innehållade orden target domain.

1. 16. Unsupervised Domain Adaptation for Regressive Annotation : Using Domain-Adversarial Training on Eye Image Data for Pupil Detection

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Erik Zetterström; [2023]
   Nyckelord :Neural networks; Deep learning; Convolutional neural networks; Transfer learning; Domain adaptation; Unsupervised training; Adversarial training; Keypoint detection; Regression; Neurala nätverk; Djupinlärning; Faltningsnätverk; Överförningsinlärning; Domänadaptering; Oövervakad inlärning; Motstående träning; Nyckelpunktsdetektion; Regression;

   Sammanfattning : Machine learning has seen a rapid progress the last couple of decades, with more and more powerful neural network models continuously being presented. These neural networks require large amounts of data to train them. LÄS MER

3. 17. Unsupervised Domain Adaptation for 3D Object Detection Using Adversarial Adaptation : Learning Transferable LiDAR Features for a Delivery Robot

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Mattias Hansson; [2023]
   Nyckelord :Unsupervised Domain Adaptation; 3D Object Detection; Mobile Robotics; Adversarial Adaptation; Computer Vision; Oövervakad Domänanpassning; 3D Objektigenkänning; Mobila Robotar; Motspelaranpassning; Datorseende;

   Sammanfattning : 3D object detection is the task of detecting the full 3D pose of objects relative to an autonomous platform. It is an important perception system that can be used to plan actions according to the behavior of other dynamic objects in an environment. LÄS MER

4. 18. Causal Reinforcement Learning for Bandits with Unobserved Confounders

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Mingwei Deng; [2023]
   Nyckelord :;

   Sammanfattning : Reinforcement Learning (RL) has been recognized as a valuable tool in various fields. However, its application is limited by its reliance on extensive data through a trial-and-error approach and challenges in generalizing learned policies. LÄS MER

5. 19. Semi-Supervised Domain Adaptation for Semantic Segmentation with Consistency Regularization : A learning framework under scarce dense labels

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Daniel Morales Brotons; [2023]
   Nyckelord :Domain Adaptation; Semi-Supervised Learning; Semi-Supervised Domain Adaptation; Semantic Segmentation; Consistency Regularization; Domain Adaptation; Semi-Supervised Learning; Semi-Supervised Domain Adaptation; Semantisk Segmentering; Konsistensregularisering;

   Sammanfattning : Learning from unlabeled data is a topic of critical significance in machine learning, as the large datasets required to train ever-growing models are costly and impractical to annotate. Semi-Supervised Learning (SSL) methods aim to learn from a few labels and a large unlabeled dataset. LÄS MER

7. 20. Ethical Hacking of a Virtual Reality Headset

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Olivia Höft; [2023]
   Nyckelord :Cyber Security; Ethical Hacking; Penetration Test; Virtual Reality Headset; Cyber Säkerhet; Etisk Hackning; Penetrationstest; Virituella Verklighets Glasögon;

   Sammanfattning : Weak product cybersecurity is an increasing problem within society, and a growing consumer product is the Virtual Reality (VR) headset. This thesis investigated common vulnerabilities in Internet of Things (IoT) consumer products and performed proof-of-concept exploits on the Meta Quest VR headset. LÄS MER