Sökning: "SimCLR"

Visar resultat 1 - 5 av 7 uppsatser innehållade ordet SimCLR.

  1. 1. Self-supervised representation learning from electrocardiogram data for medical applications

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Matilda Andersson; [2023]
    Nyckelord :Self-supervised learning; Deep learning; Cardiovascular disease; Electrocardiogram; ECG; SimCLR; BYOL; VICReg; Mathematics and Statistics;

    Sammanfattning : Cardiovascular diseases are the leading cause of death worldwide, increasing yearly. However, many abnormalities in heart cycles can be discovered and treated years before the onset of diseases. But in most societies, regular health checkups are a concept reserved for cars, not humans. LÄS MER

  3. 2. Automated Foreign Object Detection on Conveyor Belts

    Uppsats för yrkesexamina på avancerad nivå, Luleå tekniska universitet/Institutionen för system- och rymdteknik

    Författare :Kim Sundelius; [2023]
    Nyckelord :Machine Learning; Deep Learning; Object Detection; Anomaly Detection;

    Sammanfattning : Ore is transported using belt conveyor systems. The transported ore has various anomalous objects that must be removed to prevent damage to the system. Currently anomalies are detected manually using humans. This leads to increased costs of wages and damage to the system overmissed anomalies. LÄS MER

  4. 3. Feature extraction from MEG data using self-supervised learning : Investigating contrastive representation learning methods to f ind informative representations

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

    Författare :Wilhelm Ågren; [2022]
    Nyckelord :Machine learning; Deep learning; Self-supervised learning; Cluster analysis; SimCLR; Magnetoencephalography; Partial sleep deprivation; Wavelet transform; Maskininlärning; Djupinlärning; Självövervakad inlärning; Klusteranalys; SimCLR; Magnetoencefalografi; Delvis sömndeprivering; Wavelet transform;

    Sammanfattning : Modern day society is vastly complex, with information and data constantly being posted, shared, and collected everywhere. There is often an abundance of massive amounts of unlabeled data that can not be leveraged in a supervised machine learning context. LÄS MER

  5. 4. Impact of model architecture and data distribution on self-supervised federated learning

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Karin Bergdahl; [2022]
    Nyckelord :machine learning; federated learning; self-supervised learning; Technology and Engineering;

    Sammanfattning : Data is a crucial resource for machine learning. But in many settings, such as in healthcare or on mobile devices, there are obstacles that make it difficult to utilize the available data. This data is often distributed between many clients and private, meaning that central storage of the data is inadvisable. LÄS MER

  7. 5. Evaluating the effects of data augmentations for specific latent features : Using self-supervised learning

    M1-uppsats, KTH/Hälsoinformatik och logistik

    Författare :Markus Ingemarsson; Jacob Henningsson; [2022]
    Nyckelord :Contrastive learning; data augmentations; deep learning; invariant features; machine learning; representation similarity analysis; self-supervised learning; SimCLR; Kontrast inlärning; datamodifieringar; djupinlärning; maskininlärning; SimCLR; självövervakat lärande; oföränderliga egenskaper; representativ likhetsanalys;

    Sammanfattning : Supervised learning requires labeled data which is cumbersome to produce, making it costly and time-consuming. SimCLR is a self-supervising framework that uses data augmentations to learn without labels. This thesis investigates how well cropping and color distorting augmentations work for two datasets, MPI3D and Causal3DIdent. LÄS MER