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.
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Showing 1 to 20 of 128 for “"Generative adversarial networks."”.
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Belief propagation generative adversarial networks
Generative adversarial networks (GANs) are a class of generative models based on a minimax game. They have led to significant improvement in the field of unsupervised learning, especially image generation. However, most works in GANs are based on learning the distribution of the input dataset …
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ASSET PRICING OPTIMIZATION THROUGH GENERATIVE ADVERSARIAL NETWORKS
… we study the asset pricing optimisation through Generative Adversarial Networks (GAN). We have demonstrated that shallow learning can deliver similar performance for test data as compared to deep learning considered in the literature, with the added benefit of mitigating common challenges such as …
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Generative adversarial networks for fine art generation
Generative Adversarial Networks (GANs), a generative modelling technique most commonly used for image generation, have recently been applied to the task of fine art generation. Wasserstein GANs and GANHack techniques have not been applied in GANs that generate fine art, despite their showing …
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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 …
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Generating Synthetic X-rays Using Generative Adversarial Networks
… hand poses and train general-purpose Conditional Generative Adversarial Networks (CGANs) as well as our own novel network pix2xray. Our results show the successful plausibility of generating X-rays from point cloud and RGB images. We also demonstrate the superiority of our pix2xray approach, …
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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 …
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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 …
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Synthetic Electronic Medical Record Generation using Generative Adversarial Networks
… call Improved Correlation Capturing Wasserstein Generative Adversarial Network (SCorGAN) to create EHR data. This work, leverages Deep Convolutional Neural Networks to extract and understand spatial dependencies in EHR data. To improve our model's performance, we focus on our Deep Convolutional …
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Applying generative adversarial networks to intelligent subsurface imaging and identification
… on the generation of realistic GPR data using Generative Adversarial Networks. An innovative GAN ar- chitecture is proposed for generating GPR B-scans, which is, to the author’s knowledge, the first successful application of GAN to GPR B-scans. As one of the major contri- butions, a novel loss …
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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 …
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Unsupervised Text Translation Through the Application of Generative Adversarial Networks
… translation, one approach involves the use of generative adversarial techniques for sequence generation. Unfortunately, prior work using these techniques suffer from poor alignment and training instability. This thesis proposes two alternative models for unsupervised text translation that …
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Using Generative Adversarial Networks to Classify Structural Damage Caused by Earthquakes
… damage. In particular we attempt to use Generative Adversarial Neural Networks (GANs) to generate the synthetic images and enable the fast classification of rail and road damage caused by earthquakes. Fast classification of rail and road damage can allow for the safety of people and to …
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Applying generative adversarial networks to generate artificial genotype data in livestock
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01
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Stats-aware-GAN: Domain specific statistics matching in generative adversarial networks
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Unsupervised anomaly detection in multi-class datasets using Generative Adversarial Networks
"Presented in this thesis is a novel Generative Adversarial Network, or GAN, based method, D-AnoGAN, for detecting anomalies in complex datasets containing disconnected data manifolds. Current state-of-the-art methods treat disconnected data manifolds as a single, continuous one to learn from. The …
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Development and Evaluation of Generative Adversarial Networks for Predicting Central Hemodynamics
… This thesis investigates the use of generative adversarial networks (GANs) in combination with cardiovascular mechanistic models to estimate central hemodynamic values from ECG and tabular data. Three hemodynamic quantities form the focus of this work: mean pulmonary artery pressure …
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Dynamic image crowd representations for improved anomaly detection using generative adversarial networks
… translation using CGANs (Conditional generative adversarial nets) for anomaly detection within crowds, and the proposed framework is evaluated on benchmark datasets as well as the AHDCrowd dataset. The applied experiments evaluate the effectiveness of utilising various types of dynamic …
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Training large-scale video generative adversarial networks for high quality video synthesis
… task for the computer vision community. Generative Adversarial Networks have been proved effective for generating high fidelity photo-realistic images. Recently, many video synthesis models achieve high fidelity and resolution samples by carrying the success of Generative Adversarial …
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An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk
A generative adversarial network (GAN) is an implicit generative model made up of two neural networks. This minor dissertation applies GANs to recover target statistical distributions. GANs have a distinctive training architecture designed to create examples that reproduce target data samples. …
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