Essays about: "Semi- Övervakad Inlärning"
Showing result 11 - 15 of 19 essays containing the words Semi- Övervakad Inlärning.
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11. On the effectiveness of ß-VAEs for imageclassification and clustering : Using a disentangled representation for Transfer Learning and Semi-Supervised Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Data labeling is a critical and costly process, thus accessing large amounts of labeled data is not always feasible. Transfer Learning (TL) and Semi-Supervised Learning (SSL) are two promising approaches to leverage both labeled and unlabeled samples. READ MORE
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12. Telecom Equipment Segmentation and Detection Using Drone Images
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : An automated AI solution for out-door Telecom equipment segmentation is beneficial to most of the workflow for site survey and engineering performed by human. AI solutions that perform segmentation tasks are today trained with supervised learning which requires manually labeled images. READ MORE
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13. Enhancing Deep Active Learning Using Selective Self-Training For Image Classification
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : A high quality and large scale training data-set is an important guarantee to teach an ideal classifier for image classification. Manually constructing a training data- set with appropriate labels is an expensive and time consuming task. READ MORE
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14. Semi-Supervised Domain Adaptation for Pick Classification in Pick and Place Machines
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Pick and Place (PnP) machines collect and use large amounts of image data of Printed Circuit Board (PCB) components. The data is used to train automated image analysis methods to improve the decisions in the mounting process. Previous work with Neural Networks has shown promising results in the classification of the component status. READ MORE
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15. Towards unification of organ labeling in radiation therapy using a machine learning approach based on 3D geometries
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In radiation therapy, it is important to control the radiation dose absorbed by Organs at Risk (OARs). The OARs are represented as 3D volumes delineated by medical experts, typically using computed tomography images of the patient. READ MORE
