Sökning: "Oövervakad domänanpassning"

Hittade 3 uppsatser innehållade orden Oövervakad domänanpassning.

  1. 1. Real-time Unsupervised Domain Adaptation

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

    Författare :Marc Botet Colomer; [2023]
    Nyckelord :Unsupervised Domain Adaptation; Real-Time applications; Online Learning; Self-Learning; Semantic Segmentation; Reinforcement Learning; Oövervakad domänanpassning; Realtidsapplikationer; Onlineinlärning; Självinlärning; Semantisk Segmentering; Förstärkningsinlärning;

    Sammanfattning : Machine learning systems have been demonstrated to be highly effective in various fields, such as in vision tasks for autonomous driving. However, the deployment of these systems poses a significant challenge in terms of ensuring their reliability and safety in diverse and dynamic environments. LÄS MER

  3. 2. 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. 3. Learning from Synthetic Data : Towards Effective Domain Adaptation Techniques for Semantic Segmentation of Urban Scenes

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

    Författare :Gerard Valls I Ferrer; [2021]
    Nyckelord :Semantic Segmentation; Synthetic Data; Autonomous Driving; Domain Shift; Domain Adaptation; Domain Generalisation; Semantisk Segmentering; Syntetiska Data; Autonom Körning; Domänskift; Domänanpassning; Domängeneralisering;

    Sammanfattning : Semantic segmentation is the task of predicting predefined class labels for each pixel in a given image. It is essential in autonomous driving, but also challenging because training accurate models requires large and diverse datasets, which are difficult to collect due to the high cost of annotating images at pixel-level. LÄS MER