Sökning: "self-supervised learning"

Visar resultat 1 - 5 av 13 avhandlingar innehållade orden self-supervised learning.

1. 1. Self-supervised deep learning and EEG categorization

   Författare :Mats Svantesson; Magnus Thordstein; Håkan Olausson; Anders Eklund; Gerald Cooray; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; EEG; Deep Learning; Self-supervised; Interrater Agreement; T- SNE;

   Sammanfattning : Deep learning has the potential to be used to improve and streamline EEG analysis. At the present, classifiers and supervised learning dominate the field. Supervised learning depends on target labels which most often are created by human experts manually classifying data. LÄS MER

3. 2. Self-supervised Representation Learning for Visual Domains Beyond Natural Scenes

   Författare :Prakash Chandra Chhipa; Marcus Liwicki; Seiichi Uchida; Rajkumar Saini; Josep Lladós; Luleå tekniska universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; self-supervised learning; representation learning; computer vision; learning with few labels; Maskininlärning; Machine Learning;

   Sammanfattning : This thesis investigates the possibility of efficiently adapting self-supervised representation learning on visual domains beyond natural scenes, e.g., medical imagining and non-RGB sensory images. LÄS MER

4. 3. Structured Representations for Explainable Deep Learning

   Författare :Federico Baldassarre; Hossein Azizpour; Josephine Sullivan; Kevin Smith; Hamed Pirsiavash; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Explainable AI; Deep Learning; Self-supervised Learning; Transformers; Graph Networks; Computer Vision; Explainable AI; Deep Learning; Self-supervised Learning; Transformers; Graph Networks; Computer Vision; Datalogi; Computer Science;

   Sammanfattning : Deep learning has revolutionized scientific research and is being used to take decisions in increasingly complex scenarios. With growing power comes a growing demand for transparency and interpretability. The field of Explainable AI aims to provide explanations for the predictions of AI systems. LÄS MER

5. 4. Geometric Supervision and Deep Structured Models for Image Segmentation

   Författare :Måns Larsson; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; conditional random fields; convolutional neural networks; deep structured models; Semantic segmentation; self-supervised learning; supervised learning; semi-supervised learning;

   Sammanfattning : The task of semantic segmentation aims at understanding an image at a pixel level. Due to its applicability in many areas, such as autonomous vehicles, robotics and medical surgery assistance, semantic segmentation has become an essential task in image analysis. LÄS MER

7. 5. Deep Learning for Digital Pathology in Limited Data Scenarios

   Författare :Karin Stacke; Jonas Unger; Gabriel Eilertsen; Claes Lundström; Henning Müller; Linköpings universitet; []
   Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; Medical imaging; Digital pathology; Radiology; Machine learning; Deep learning.;

   Sammanfattning : The impressive technical advances seen for machine learning algorithms in combination with the digitalization of medical images in the radiology and pathology departments show great promise in introducing powerful image analysis tools for image diagnostics. In particular, deep learning, a subfield within machine learning, has shown great success, advancing fields such as image classification and detection. LÄS MER