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 344 for “"Synthetic Data"”.
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Generating Trustworthy Synthetic Data
Science relies on data. Real data can be severely limiting: it may be privacy-sensitive, unfair, unbalanced, unrepresentative, or it may simply not exist for the setting of interest. These limitations continuously constrain how the machine learning (ML) community operates---from training to …
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Visual Representation Learning from Synthetic Data
… largely depends on the quality and quantity of data. Synthetic data presents unique advantages in terms of flexibility, scalability, and controllability. Recent advances in generative modeling have enabled the synthesis of photorealistic images and high-quality text, drastically increasing the …
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Towards Creating Synthetic Data Testbeds for Research
Insurance datasets are generally private in order to protect user information, making it difficult for the ML research community to access and experiment with this data. To increase accessibility and innovation on private insurance data, we compile and share publicly available insurance datasets, …
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Harnessing Synthetic Data for Robust and Reliable Vision
… by models trained on large amounts of exemplar data for different tasks. These exemplar data sources intend to capture task-specific information and instance-level variations that a trained model will likely encounter in the wild. However, for conditions where curating lots of labeled real-world …
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Differentially Private Synthetic Data Generation for Relational Databases
Existing differentially private (DP) synthetic data generation mechanisms typically assume a single-source table. In practice, data is often distributed across multiple tables with relationships across tables. This study presents the first-of-its-kind algorithm that can be combined with \emph{any} …
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Specialization of Vision Representations with Personalized Synthetic Data
… vision tasks, which are both fine-grained and data-scarce. Recent works have successfully applied synthetic data to general-purpose representation learning, while advances in Text-to-Image (T2I) diffusion models have enabled the generation of personalized images from just a few real examples. …
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How Transferable are Video Representations Based on Synthetic Data?
… improved dramatically with massive-scale video datasets. Yet, these datasets are accompanied with issues related to curation cost, privacy, ethics, bias, and copyright. Compared to that, only minor efforts have been devoted toward exploring the potential of synthetic video data. In this work, as …
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SDV : an open source library for synthetic data generation
… a robust system that can accurately generate synthetic data. The goals of this thesis were to separate the different components in synthetic data generation into their own libraries. We identified these components as consisting of a way to transform the data, a way to model the data, and a way …
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The Synthetic Data Vault : generative modeling for relational databases
… is to build a system that automatically creates synthetic data for enabling data science endeavors. To meet this goal, we present the Synthetic Data Vault (SDV), a system that builds generative models of relational databases. We are able to sample from the model and create synthetic data, hence …
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Procedural synthetic data for self-driving cars using 3D graphics
… configurable system for procedurally generating synthetic data for self-driving vehicles. To address the problem of data hungry vision-based learning algorithms used in self-driving vehicles, we develop a system that generates synthetic images, including class level annotations, of street scenes. …
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Learning to solve problems in computer vision with synthetic data
… to reach their full potential, a large enough dataset must be available. This poses severe limitation over problems that DNN can be applied to. Fortunately, many problems in computer vision have well-understood physical models, and can be simulated readily. This thesis considers the use of …
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Learning New Dimensions of Human Visual Similarity using Synthetic Data
… holistically. Our first step is to collect a new dataset of human similarity judgments over image pairs that are alike in diverse ways. Critical to this dataset is that judgments are nearly automatic and shared by all observers. To achieve this we use recent text-to-image models to create …
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Improving LLM Long Context Understanding via Synthetic Data and Adaptive Compression
… methodology. First, we generate long context synthetic data across a variety of tasks for training context-extended models, which can supplement or even replace expensive human-annotated data. Second, we introduce a novel two-pass, adaptive compression technique for more intelligent …
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Design and development of an LLM-based framework for synthetic data generation
The increasing demand for high-quality datasets in fields such as healthcare, finance, and cybersecurity is hindered by challenges such as data scarcity, privacy concerns, and regulatory restrictions. This thesis introduces a novel framework for generating synthetic data using fine-tuned Large …
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Detecting Errors in Financial Data: A Multi-Agent LLM and Synthetic Data Approach
… existing error detection models for transaction data often suffer from class imbalance, leading to reduced performance on minority incorrect transaction cases. To address these issues, this paper proposes two novel approaches. First, a hybrid method integrating multi-agent Large Language Models …
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WiSDM: a platform for crowd-sourced data acquisition, analytics, and synthetic data generation
… a result, it is desirable to collect behavioral data before and during a disease outbreak. Such data can help in creating better computer models that can, in turn, be used by epidemiologists and policy makers to better plan and respond to infectious disease outbreaks. However, traditional data …
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Synthetic data generation pipeline to effectively train deep learning augmented super-resolution ultrasound imaging
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01
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Synthetic Data Generation and Sampling for Online Training of DNN in Manufacturing Supervised Learning Problems
… of Industrial Internet offers abundant passive data from manufacturing systems and networks, which enables data-driven modeling with high-data-demand, advanced statistical models such as Deep Neural Networks (DNNs). Deep Neural Networks (DNNs) have proven to be remarkably effective in supervised …
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