Sökning: "Conditional generative adversarial network"

Visar resultat 1 - 5 av 12 uppsatser innehållade orden Conditional generative adversarial network.

  1. 1. Latent Data-Structures for Complex State Representation : A Steppingstone to Generating Synthetic 5G RAN data using Deep Learning

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Högenergifysik

    Författare :Jakob Häggström; [2023]
    Nyckelord :Data Science; Machine Learning; Generative models; Artificial Intelligence; 5GRAN;

    Sammanfattning : The aim of this thesis is to investigate the feasibility of applying generative deep learning models on data related to 5G Radio Access Networks (5GRAN). Simulated data is used in order to develop the generative models, and this project serves as a proof of concept for further applications on real data. LÄS MER

  3. 2. Fast Simulations of Radio Neutrino Detectors : Using Generative Adversarial Networks and Artificial Neural Networks

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Högenergifysik

    Författare :Anton Holmberg; [2022]
    Nyckelord :Askaryan emission; Radio detection of neutrinos; in-ice propagation; neutrino; Generative adversarial networks; GAN; WGAN; Neural networks; NN; surrogate model; IceCube; deep learning; generative model;

    Sammanfattning : Neutrino astronomy is expanding into the ultra-high energy (>1017eV) frontier with the use of in-ice detection of Askaryan radio emission from neutrino-induced particle showers. There are already pilot arrays for validating the technology and the next few years will see the planning and construction of IceCube-Gen2, an upgrade to the current neutrino telescope IceCube. LÄS MER

  4. 3. Geospatial Trip Data Generation Using Deep Neural Networks

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

    Författare :Aditya Deepak Udapudi; [2022]
    Nyckelord :Deep Learning; Geospatial; Generative Adversarial Network GAN ; Deep Learning; Geospatial; Generativa Motståndsnätverk GAN ;

    Sammanfattning : Development of deep learning methods is dependent majorly on availability of large amounts of high quality data. To tackle the problem of data scarcity one of the workarounds is to generate synthetic data using deep learning methods. LÄS MER

  5. 4. Towards Deep Learning Accelerated Sparse Bayesian Frequency Estimation

    Master-uppsats, Lunds universitet/Matematisk statistik

    Författare :Mika Persson; [2022]
    Nyckelord :Bayesian Statistics; Deep Learning; Frequency Estimation; Generative Adversarial Networks; Artificial Neural Networks; Statistical Modelling; Mathematics and Statistics;

    Sammanfattning : The Discrete Fourier Transform is the simplest way to obtain the spectrum of a discrete complex signal. This thesis concerns the case when the signal is known to contain a small (unknown) number of frequencies, not limited to the discrete Fourier frequencies, embedded in complex Gaussian noise. LÄS MER

  7. 5. Limitations of cGAN in functional area division for interior design

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

    Författare :Julia Sommarlund; [2022]
    Nyckelord :Neural network; Conditional generative adversarial network; Interior design; Automated design; Neurala nätverk; Villkorligt generativt motståndsnätverk; Interiördesign; Automatiserad design;

    Sammanfattning : A process that historically has been hard to automate is interior design, mainly due to its subjective nature and lack of obvious guidelines. Scientifically, there is interest to examine if subjective processes can be automated using black box algorithms such as neural networks, as well as corporate interest in this subject to increase efficiency and create systems for automated floor plan design. LÄS MER