Sökning: "CIFAR-10"

Visar resultat 1 - 5 av 27 uppsatser innehållade ordet CIFAR-10.

  1. 1. The Impact of Deep Neural Network Pruning on the Hyperparameter Performance Space: An Empirical Study

    Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

    Författare :Jonna Matthiesen; [2023-10-24]
    Nyckelord :Compression; Deep Learning; DNN; Hyperparameters; Optimization; Pruning; Hyperparameter Optimisation; Hyperparameter Tuning;

    Sammanfattning : With the continued growth of deep learning models in terms of size and computational requirements, the need for efficient models for deployment on resource-constrained devices becomes crucial. Structured pruning has emerged as a proven method to speed up models and reduce computational requirements. LÄS MER

  3. 2. Performance analysis: CNN model on smartphones versus on cloud : With focus on accuracy and execution time

    Kandidat-uppsats, Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

    Författare :Stegmayr Klas; Johansson Edwin; [2023]
    Nyckelord :CNN; Deep learning; iOS; Core ML; CIFAR-10;

    Sammanfattning : In the modern digital landscape, mobile devices serve as crucial data generators.Their usage spans from simple communication to various applications such as userbehavior analysis and intelligent applications. However, privacy concerns associatedwith data collection are persistent. LÄS MER

  4. 3. Image Colorization Based on Deep Learning

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

    Författare :Tao Deng; [2023]
    Nyckelord :Image colorization; Deep Learning; Convolutional Neural Network; Generative Adversarial Network; Färgläggning av bilder; djupinlärning; Konvolutionella Neurala Nätverk; Generativa Adversariella Nätverk;

    Sammanfattning : With the development of artificial intelligence, there is a clear trend to combine computer technology with traditional industries. In recent years, with the development of digital media technology, many methods for coloring gray-scale images have been proposed. LÄS MER

  5. 4. Prediction of the gain in classification performance from combining multiple imaging modalities

    Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

    Författare :Roman Denkin; [2023]
    Nyckelord :;

    Sammanfattning : In this work, we investigate the relationship between different image modalities and classification performance, aiming to predict the potential gain in classification accuracy when combining multiple modalities. We analyze mathematical and statistical measures and develop novel reconstruction measures (RMSE and RSSIM) to assess information distribution between different image modalities. LÄS MER

  7. 5. Federated Self-supervised Learning in Computer Vision

    Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

    Författare :Jonas Frankemölle; [2023]
    Nyckelord :;

    Sammanfattning : With an ever-increasing amount of available image data, self-supervised learning (SSL) circumvents the necessity for annotations in traditional supervised learning methods. SSL methods such as SimSiam have shown excellent results on popular benchmark datasets, even outperforming supervised methods. LÄS MER