Sökning: "3D Generativa Modeller"

Visar resultat 1 - 5 av 7 uppsatser innehållade orden 3D Generativa Modeller.

1. 1. Technology Acceptance for AI implementations : A case study in the Defense Industry about 3D Generative Models

   Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

   Författare :Michael Arenander; [2023]
   Nyckelord :Technology Acceptance; Artificial Intelligence; Machine Learning; 3D Generative Models; Innovation; Teknisk Acceptans; Artificiell Intelligens; Maskininlärning; 3D Generativa Modeller; Innovation;

   Sammanfattning : Advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has emerged into 3D object creation processes through the rise of 3D Generative Adversarial Networks (3D GAN). These networks contain 3D generative models capable of analyzing and constructing 3D objects. LÄS MER

3. 2. Exploring Normalizing Flow Modifications for Improved Model Expressivity

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

   Författare :Marcel Juschak; [2023]
   Nyckelord :Normalizing Flows; Motion Synthesis; Invertible Neural Networks; Glow; MoGlow; Maximum Likelihood Estimation; Generative models; normaliserande flöden; rörelsesyntes; inverterbara neurala nätverk; Glow; MoGlow; maximum likelihood-skattning generativa modeller;

   Sammanfattning : Normalizing flows represent a class of generative models that exhibit a number of attractive properties, but do not always achieve state-of-the-art performance when it comes to perceived naturalness of generated samples. To improve the quality of generated samples, this thesis examines methods to enhance the expressivity of discrete-time normalizing flow models and thus their ability to capture different aspects of the data. LÄS MER

4. 3. Deep Generative Modeling : An Overview of Recent Advances in Likelihood-based Models and an Application to 3D Point Cloud Generation

   Master-uppsats, Umeå universitet/Institutionen för matematik och matematisk statistik

   Författare :Shams Methnani; [2023]
   Nyckelord :;

   Sammanfattning : Deep generative modeling refers to the process of constructing a model, parameterized by a deep neural network, that learns the underlying patterns and structures of the data generating process which produced the samples in a given dataset, in order to generate novel samples that resemble those in the original dataset. Deep generative models for 3D shape generation hold significant importance to various fields including robotics, medical imaging, manufacturing, computer animation and more. LÄS MER

5. 4. Using a Deep Generative Model to Generate and Manipulate 3D Object Representation

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

   Författare :Yu Hu; [2023]
   Nyckelord :Neural networks; point cloud; 3D shape generation; 3D shape manipulation; classification; Neurala nätverk; punktmoln; generering av 3D-former; manipulation av 3Dformer; klassificering;

   Sammanfattning : The increasing importance of 3D data in various domains, such as computer vision, robotics, medical analysis, augmented reality, and virtual reality, has gained giant research interest in generating 3D data using deep generative models. The challenging problem is how to build generative models to synthesize diverse and realistic 3D objects representations, while having controllability for manipulating the shape attributes of 3D objects. LÄS MER

7. 5. 3D Dose Prediction from Partial Dose Calculations using Convolutional Deep Learning models

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

   Författare :Sergio Felipe Liberman Bronfman; [2021]
   Nyckelord :Machine learning; Modeling and simulation; Life and medical sciences; Predictive models; Artificial Neural Networks; Physics computing; Maskininlärning; Modellering och simulering; Livs och medicinska vetenskaper; Förutsägbara modeller; Artificiellt neurala nätverk; Fysikberäkning;

   Sammanfattning : In this thesis, the problem of predicting the full dose distribution from a partially modeled dose calculation is addressed. Two solutions were studied: a vanilla Hierarchically Densely Connected U-net (HDUnet) and a Conditional Generative Adversarial Network (CGAN) with HDUnet as a generator. LÄS MER