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 76 for “"Generative Adversarial Network"”.
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Generative Adversarial Network (GAN) for Medical Image Synthesis and Augmentation
… on further improving this technology. Generative adversarial network (GAN) is a DNN framework for data synthetization, which provides a practical solution for medical image augmentation and translation. In this study, we first perform a quantitative survey on the published studies on …
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Single magnetic resonance image super-resolution using generative adversarial network
… of these medical images, we have adapted a Generative Adversarial Network (GAN) model where the generator has a DenseNet type structure and the discriminator is based on the U-Net model. We have used a combination of loss functions to ensure the generated images are consistent with ground …
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Self-attentive generative adversarial network-based authorship attribution in historical texts
… deep learning, specifically a Self-Attentive Generative Adversarial Network (GAN), this research proposes a novel methodology – Reverse Authorship Attribution Technique (RAAT) – to identify and mitigate attempts to hide or mimic authorial style. When authors deliberately hide their identity, …
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Generated Image Quality Assurance and Generative Adversarial Network Augmentation for Machine Learning
… data quality and augmentation techniques using Generative Adversarial Networks (GANs), pecifically focusing on the Pix2Pix architecture. The research addresses the critical challenges of improving image quality, optimizing hyperparameters, and detecting fake images generated by GANs, aiming to …
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A New Generative Adversarial Network for Improving Classification Performance for Imbalanced Data
… class. Various sectors have utilised deep neural networks for data synthesis. However, according to research papers in these fields, balanced data outperforms imbalanced data when it comes to deep neural networks. Although deep generative approaches, such as Generative Adversarial Networks (GANs), …
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Machine learning in housing design : exploration of generative adversarial network in site plan / floorplan generation
… the design process, which is a combination of Generative Adversarial Network (Pix2Pix), Bayesian Network and Evolutionary Algorithm.
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Generative adversarial modeling of 3D shapes
… In this thesis, we propose two models, 3D Generative Adversarial Network and ShapeHD, to learn shape priors from existing 3D shapes via generative-adversarial modeling, pushing the limits of shape generation, single-view shape completion and reconstruction. For shape generation, we …
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ENHANCING BUILDING FAULT DETECTION, DIAGNOSTICS, AND PROGNOSTICS THROUGH A HYBRID PHYSICS-INFORMED MODELING FRAMEWORK FOR OVERCOMING QUANTITY AND TEMPORAL DATA SCARCITY
… and temporal sparsity. First, Hybrid Conditional Generative Adversarial Network (HCGAN) is developed. By leveraging physics-based simulations as conditional priors, it generates high-fidelity synthetic fault data, enabling zero-shot diagnosis without requiring real fault samples. Second, to …
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Advance metabolite identification from tandem mass spectra using deep generative models
… of metabolites and spectra. Further, we build a generative adversarial network (GAN) to optimize the classifier as the discriminator. A large number of experiments are conducted. The experimental results verify the effectiveness of our tool.
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Deep Learning Image Augmentation using Inpainting with Partial Convolution and GANs
… (1) inpainting using partial convolution and (2) generative adversarial network (GAN) to generate synthetic data to train deep learning image classifiers. We show that the addition of synthetic training images dramatically improved the accuracies of our defect classifiers. Using Gradient- Class …
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Deep learning for downscaling GOES-18 measurements for wildfire detection
… to a spatial resolution of 375 meters using a Generative Adversarial Network (GAN). High-resolution VIIRS images are used as ground truth labels during the training phase. Experimental results demonstrate that the proposed framework successfully enhances the spatial resolution of GOES ABI data …
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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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Fast modeling of multi-phase mixture transport in piston/ring/liner system via GAN-augmented progressive modeling
… vortices near ring gaps by a physics-informed Generative Adversarial Network, and 3) established a lower bound estimation of oil consumption based on the "healthy system" oil distribution pattern. This thesis provides a powerful modeling methodology that can achieve fast modeling and monitoring …
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POCS Augmented CycleGAN for MR Image Reconstruction
… combines two state-of-the-art deep learning networks, U-Net and Generative Adversarial Network with Cycle loss (CycleGAN), with a traditional data reconstruction method: Projection Onto Convex Sets (POCS). Experiments were then performed to evaluate the method by comparing it to several …
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Simplifying Multimodal Emotion Recognition with Single Eye Movement Modality
… compromising the performance, we propose a generative adversarial network-based framework. In our model, a single modality of eye movements is used as input and it is capable of mapping the information onto multimodal features. Experimental results on SEED series datasets with different …
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Network motif prediction using generative models for graphs
… interactions between different entities in networks. Generative graph models create new graphs that mimic the properties of already existing graphs. Generative models are successful at retaining the pairwise interactions of the underlying networks but often fail to capture higher-order …
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Multi-Agent Deep Reinforcement Learning and GAN-Based Market Simulation for Derivatives Pricing and Dynamic Hedging
… to learn. We explore the implementation of a generative adversarial network-based approach to generate realistic market data from past historical data. This data is used to train the reinforcement learning framework and evaluate its robustness. The results demonstrate the efficacy of deep …
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Data-Driven Characterization of Micro-structural Shape and Topology in Engineering Materials
… via deep learning models such as Progressive Generative Adversarial Network (PGAN) and Denoising Diffusion Probabilistic Models (DDPM). Both Hu moments and PEM portray a high degree of invariance to rotation and scaling, showing considerable effectiveness in capturing morphologic features like …
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