Sökning: "generative adversarial learning"

Visar resultat 1 - 5 av 8 uppsatser innehållade orden generative adversarial learning.

  1. 1. Generation of Synthetic Images with Generative Adversarial Networks

    Master-uppsats, Blekinge Tekniska Högskola/Institutionen för datalogi och datorsystemteknik

    Författare :Mousa Zeid Baker; [2018]
    Nyckelord :classification; deep learning; generative adversarial network; machine learning;

    Sammanfattning : Machine Learning is a fast growing area that revolutionizes computer programs by providing systems with the ability to automatically learn and improve from experience. In most cases, the training process begins with extracting patterns from data. The data is a key factor for machine learning algorithms, without data the algorithms will not work. LÄS MER

  2. 2. Cell Image Transformation Using Deep Learning

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Emmy Sjöstrand; Jesper Jönsson; [2018]
    Nyckelord :Mathematics and Statistics;

    Sammanfattning : This thesis was written at CellaVision who sells digital microscope systems, mainly used for blood analysis. Blood tests are an important part of modern health care and today digital microscopes are widely used to replace conventional microscopy. LÄS MER

  3. 3. Updating the generator in PPGN-h with gradients flowing through the encoder

    Uppsats för yrkesexamina på avancerad nivå, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Hesam Pakdaman; [2018]
    Nyckelord :Computer Science; Computer Vision; Deep Learning; Machine Learning; Generative Adversarial Networks; GAN; Neural Networks; Generative models;

    Sammanfattning : The Generative Adversarial Network framework has shown success in implicitly modeling data distributions and is able to generate realistic samples. Its architecture is comprised of a generator, which produces fake data that superficially seem to belong to the real data distribution, and a discriminator which is to distinguish fake from genuine samples. LÄS MER

  4. 4. Generative adversarial networks for single image super resolution in microscopy images

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

    Författare :Saurabh Gawande; [2018]
    Nyckelord :Deep Learning; Generative adversarial networks; Super resolution; High content screening microscopy; Deep Learning; Generative adversarial networks; Super resolution; High content screening microscopy;

    Sammanfattning : Image Super resolution is a widely-studied problem in computer vision, where the objective is to convert a lowresolution image to a high resolution image. Conventional methods for achieving super-resolution such as image priors, interpolation, sparse coding require a lot of pre/post processing and optimization. LÄS MER

  5. 5. Automotive 3D Object Detection Without Target Domain Annotations

    Master-uppsats, Linköpings universitet/Datorseende; Linköpings universitet/Datorseende

    Författare :Fredrik Gustafsson; Erik Linder-Norén; [2018]
    Nyckelord :Object Detection; 3D Object Detection; Domain Adaptation; Generative Adversarial Networks; Computer Vision; Deep Learning; Machine Learning; Autonomous Driving;

    Sammanfattning : In this thesis we study a perception problem in the context of autonomous driving. Specifically, we study the computer vision problem of 3D object detection, in which objects should be detected from various sensor data and their position in the 3D world should be estimated. LÄS MER


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