Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 48 for “"Generative Adversarial Networks (GANs)"”.
-
Natural video synthesis with Generative Adversarial Networks
Generative Adversarial Networks (GANs) are the state of the art neural network models for image generation, but the use of GANs for video generation is still largely unexplored. This thesis introduces new GAN based video generation methods by proposing the technique of model inflation and the …
-
Machine Learning for 3D Visualisation Using Generative Models
… in the past ten years is the introduction of generative adversarial networks (GANs), which had achieved great success in their ability to generate images comparable to real photos with minimum human intervention. These networks can generalise to a multitude of desired outputs, especially in …
-
Machine Learning for 3D Visualisation Using Generative Models
… in the past ten years is the introduction of generative adversarial networks (GANs), which had achieved great success in their ability to generate images comparable to real photos with minimum human intervention. These networks can generalise to a multitude of desired outputs, especially in …
-
Generative methods for image synthesis with applications to medical imaging
… for diagnostic and analytical improvements. Generative methods for image synthesis have been an active area of research in recent years. In this thesis, we explore the use of a hybrid model of generative adversarial networks (GANs) and transformer networks for image synthesis. We propose a …
-
Using generative modelling in healthcare
… We shall also present two popular classes of generative models, namely Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) and their variants. Subsequently, we shall review some new developed imputation methods which are based on GANs and VAEs. We shall assess their …
-
AI-Assisted Pipeline for 3D Face Avatar Generation
… and human labour. This thesis leverages generative models like Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) to automate avatar creation. Our pipeline offers control over three aspects: face shape, skin color, and fine details like beards or wrinkles. This …
-
Using High-Performance Computing to Scale Generative Adversarial Networks
Generative adversarial networks(GANs) are methods that can be used for data augmentation, which helps in creating better detection models for rare or imbalanced datasets. They can be difficult to train due to issues such as mode collapse. We aim to improve the performance and accuracy of the …
-
Light source relighting for indoor scene photos with deep neural networks
We seek to use deep neural networks to develop a method to detect the light sources in a given image of an indoor scene, computationally adjust their lighting intensity, and re-render the edited scene as an image. By doing so, we can visually relight the image--effectively turning the light source …
-
Machine Learning-Aided Aerospace Applications with Generative Adversarial Networks
<p>Applied generative machine-learning models have demonstrated exceptional accuracy at recreating realistic data, becoming a highly researched field in aerospace and defense technologies. Generative Adversarial Networks (GANs), a subset of generative models, have shown remarkable proficiency at …
-
Recovery of high-resolution magnetic field distribution inside the brain from limited MRI data using machine learning prior
… of scan time. Recent advances in deep neural networks, particularly the generative adversarial networks (GANs), can learn the prior information through examples and generate the high-resolution field map using only one low-resolution field map counter- part. In this work, we apply the deep …
-
3D reconstruction of human body via machine learning
… multi-person linear model (SMPL) model by the generative adversarial networks (GANs). The 3D facial reconstruction used the morphable facial model by principal component analysis (PCA) and the LS3D-W database. The 3D garments are reconstructed by the multi-garment net (MGN) to generate …
-
Concepts from unclear textual embeddings for text-to-image synthesis
… made in this direction specifically by using Generative Adversarial Networks(GANs). Although current state of the art models can generate images that roughly adhere to the textual description, there still remains a long way to go, in terms of producing high quality images that adhere to the …
-
Generative models for predictive UI design tools
… be arranged and styled. This paper introduces a generative model approach to predictive design for mobile UI layouts. Given a partial UI design, the model predicts the next UI element that should be added to the layout. Moreover, the model can be used queried multiple times in succession to …
-
Missing channel reconstruction for sloan digital sky survey images using linear models and generative adversarial networks
… are examined. For the deep learning model, the generative adversarial networks (GANs) with U-Net are deployed in the experiment. Several preprocessing techniques including normalization and cropping are done before feeding the images into the model. The results indicate that both methods can …
-
PacGAN: The power of two samples in generative adversarial networks
Generative adversarial networks (GANs) are innovative techniques for learning generative models of complex data distributions from samples. Despite remarkable recent improvements in generating realistic images, one of their major shortcomings is the fact that in practice, they tend to produce …
-
High resolution millimeter wave imaging for self-driving cars
… recent advances in deep learning known as Generative Adversarial Networks (GANs). HawkEye introduces a GAN architecture that is customized to mmWave imaging and builds a system that can significantly enhance the quality of mmWave images for self-driving cars.
-
Generative modelling under epistemic uncertainty
… these principles through Random-Set Neural Networks (RS-NN) for classification and a Unified Evaluation Framework, the primary contribution of this work lies in re-imagining Generative AI under epistemic uncertainty. We introduce Random-Set Large Language Models (RS-LLMs), which predict …
-
Deep Representation Learning on Labeled Graphs
… challenging for ICA. As a new way to train generative models, generative adversarial networks (GANs) have achieved considerable success in image generation, and this framework has also recently been applied to data with graph structures. We identify the drawbacks of existing deep frameworks …
-
Deidentification of Face Videos in Naturalistic Driving Scenarios
… this issue, we leverage recent advancements in generative adversarial networks (GANs) and demonstrate their effectiveness in deidentifying individuals by swapping their faces with those of others. Extensive experimentation is conducted using a large-scale dataset from ORNL, enabling the …
-
Quantitative and Qualitative Analysis of Text-to-Image models
… progress recently, including great strides with generative models like Generative Adversarial Networks (GANs), Diffusion Models, and Transformers. These models have shown they can create high-quality images from a variety of text prompts. However, a comprehensive analysis that examines both their …
Page 1 of 3