Sökning: "Automatisk märkning"

Visar resultat 1 - 5 av 9 uppsatser innehållade orden Automatisk märkning.

1. 1. Meta-Pseudo Labelled Multi-View 3D Shape Recognition

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

   Författare :Fehmi Ayberk Uçkun; [2023]
   Nyckelord :3D shape recognition; 3D object classification; 3D shape retrieval; 3D object retrieval; Automatic labelling; Semi-supervised learning; Pseudo labelling; Meta Pseudo Labelling; Multi-View Convolutional Neural Networks; Shape descriptors; Multi-view representations; Deeplearning; 3D-formigenkänning; 3D-objektklassificering; 3D-formhämtning; Hämtning av 3D-objekt; Automatisk märkning; Halv-vägledd lärning; Pseudomärkning; Meta Pseudo-märkning; Multi-View Faltningsnät; Formbeskrivningar; Multi-view representation; Djupinlärning;

   Sammanfattning : The field of computer vision has long pursued the challenge of understanding the three-dimensional world. This endeavour is further fuelled by the increasing demand for technologies that rely on accurate perception of the 3D environment such as autonomous driving and augmented reality. LÄS MER

3. 2. Self-Supervised Fine-Tuning of sentence embedding models using a Smooth Inverse Frequency model : Automatic creation of labels with Smooth Inverse Frequency model

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

   Författare :Vittorio Pellegrini; [2023]
   Nyckelord :Natural Language Processing; sentence embeddings; Transformer-based architectures; sentence paraphrasing; sentence similarity; sentence clustering; Naturlig språkbehandling; inbäddning av meningar; transformatorbaserade arkitekturer; parafrasering av meningar; meningslikhet; klustring av meningar Canvas Lärplattform; Dockerbehållare; Prestandajustering;

   Sammanfattning : Sentence embedding models play a key role in the field of Natural Language Processing. They can be exploited for the resolution of several tasks like sentence paraphrasing, sentence similarity, and sentence clustering. LÄS MER

4. 3. How to Estimate Local Performance using Machine learning Engineering (HELP ME) : from log files to support guidance

   Master-uppsats, Linköpings universitet/Artificiell intelligens och integrerade datorsystem

   Författare :Hugo Ekinge; [2023]
   Nyckelord :Machine learning; GRU; 1D-CNN; Transformer; log analysis; parameter estimation; regression; performance monitoring; deep learning; troubleshooting; support; Maskininlärning; GRU; 1D-CNN; Transformer; logganalys; parameteruppskattning; regression; prestandaövervakning; djupinlärning; felsökning; support;

   Sammanfattning : As modern systems are becoming increasingly complex, they are also becoming more and more cumbersome to diagnose and fix when things go wrong. One domain where it is very important for machinery and equipment to stay functional is in the world of medical IT, where technology is used to improve healthcare for people all over the world. LÄS MER

5. 4. Labelling Motion Capture Markers Using Dynamic Graph Convolutional Neural Networks

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

   Författare :Jacob Stuart; [2022]
   Nyckelord :;

   Sammanfattning : This thesis concerns labelling unlabelled motion capture (mocap) data using a Dynamic Graph Convolutional Neural Network (DGCNN) [46]. The most common type of motion-capture system, i.e. passive motion-capture, records the 3D position of multiple reflective markers using multiple infrared cameras with overlapping fields of view. LÄS MER

7. 5. Expressive Automatic Music Transcription : Using hard onset detection to transcribe legato slurs for violin

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

   Författare :Simon Falk; [2022]
   Nyckelord :Automatic Music Transcription; Signal Processing; Convolutional Neural Networks; Onset Detection; Music Information Retrieval; Automatisk musiktranskription; Signalbearbetning; Faltningsnätverk; Ansatsdetektion; Informationssökning av musik;

   Sammanfattning : Automatic Music Transcriptions systems such as ScoreCloud aims to convert audio signals to sheet music. The information contained in sheet music can be divided into increasingly descriptive layers, where most research on Automatic Music Transcription is restricted on note-level transcription and disregard expressive markings such as legato slurs. LÄS MER