• Login
    View Item 
    •   Home
    • Theses and Dissertations
    • PhD Dissertations
    • View Item
    •   Home
    • Theses and Dissertations
    • PhD Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Generative Models for Neural Fields

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Generative Models for Neural Fields.pdf
    Size:
    64.78Mb
    Format:
    PDF
    Description:
    Generative Models for Neural Fields - PhD thesis
    Download
    Type
    Dissertation
    Authors
    Skorokhodov, Ivan cc
    Advisors
    Wonka, Peter cc
    Elhoseiny, Mohamed cc
    Committee members
    Ghanem, Bernard cc
    Heidrich, Wolfgang cc
    Niessner, Matthias
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2023-02
    Permanent link to this record
    http://hdl.handle.net/10754/690419
    
    Metadata
    Show full item record
    Abstract
    Deep generative models are deep learning-based methods that are optimized to synthesize samples of a given distribution. During the past years, they have attracted a lot of interest from the research community, and the developed tools now enjoy many practical applications in content creation and editing. In computer vision, such models are typically built for images, videos, and 3D objects. Recently, there has emerged a paradigm of neural fields, which unifies the representations of such types of data by parametrizing them via neural networks. In this work, we develop generative modeling methods for images, videos, and 3D objects which treat the underlying data in such a form. We show that this perspective can yield state-of-the-art synthesis quality and many useful practical benefits, like interpolation/extrapolation capabilities, geometric inductive biases, and more efficient training and inference.
    Citation
    Skorokhodov, I. (2023). Generative Models for Neural Fields [KAUST Research Repository]. https://doi.org/10.25781/KAUST-54DV8
    DOI
    10.25781/KAUST-54DV8
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-54DV8
    Scopus Count
    Collections
    PhD Dissertations; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     

    Related items

    Showing items related by title, author, creator and subject.

    • Thumbnail

      Reduced resilience of a globally distributed coccolithophore to ocean acidification: Confirmed up to 2000 generations, supplement to: Jin, Peng; Gao, Kunshan (2016): Reduced resilience of a globally distributed coccolithophore to ocean acidification: Confirmed up to 2000 generations. Marine Pollution Bulletin, 103(1-2), 101-108

      Jin, Peng; Gao, Kunshan (PANGAEA - Data Publisher for Earth & Environmental Science, 2016) [Dataset]
      Ocean acidification (OA), induced by rapid anthropogenic CO2 rise and its dissolution in seawater, is known to have consequences for marine organisms. However, knowledge on the evolutionary responses of phytoplankton to OA has been poorly studied. Here we examined the coccolithophore Gephyrocapsa oceanica, while growing it for 2000 generations under ambient and elevated CO2 levels. While OA stimulated growth in the earlier selection period (from generations 700 to 1550), it reduced it in the later selection period up to 2000 generations. Similarly, stimulated production of particulate organic carbon and nitrogen reduced with increasing selection period and decreased under OA up to 2000 generations. The specific adaptation of growth to OA disappeared in generations 1700 to 2000 when compared with that at 1000 generations. Both phenotypic plasticity and fitness decreased within selection time, suggesting that the species' resilience to OA decreased after 2000 generations under high CO2 selection.
    • Thumbnail

      M-Elfeki/GDPP: Generator loss to reduce mode-collapse and to improve the generated samples quality.

      Elfeki, Mohamed; Couprie, Camille; Elhoseiny, Mohamed; Rivière, Morgane (Github, 2018-11-30) [Software]
      Generator loss to reduce mode-collapse and to improve the generated samples quality.
    • Thumbnail

      Entropy generation minimization: A practical approach for performance evaluation of temperature cascaded co-generation plants

      Myat, Aung; Thu, Kyaw; Kim, Youngdeuk; Saha, Bidyut Baran; Ng, K. C. (Energy, Elsevier BV, 2012-10) [Article]
      We present a practical tool that employs entropy generation minimization (EGM) approach for an in-depth performance evaluation of a co-generation plant with a temperature-cascaded concept. Co-generation plant produces useful effect production sequentially, i.e., (i) electricity from the micro-turbines, (ii) low pressure steam at 250 °C or about 8-10 bars, (iii) cooling capacity of 4 refrigeration tones (Rtons) and (iv) dehumidification of outdoor air for air conditioned space. The main objective is to configure the most efficient configuration of producing power and heat. We employed entropy generation minimization (EGM) which reflects to minimize the dissipative losses and maximize the cycle efficiency of the individual thermally activated systems. The minimization of dissipative losses or EGM is performed in two steps namely, (i) adjusting heat source temperatures for the heat-fired cycles and (ii) the use of Genetic Algorithm (GA), to seek out the sensitivity of heat transfer areas, flow rates of working fluids, inlet temperatures of heat sources and coolant, etc., over the anticipated range of operation to achieve maximum efficiency. With EGM equipped with GA, we verified that the local minimization of entropy generation individually at each of the heat-activated processes would lead to the maximum efficiency of the system. © 2012.
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.