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 52 for “"Synthetic Dataset"”.
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Semantic amodal video segmentation using a synthetic dataset
… modal instance-level object segmentation dataset. This new dataset provides ample opportunities to train models for instance-level segmentation, both modal and amodal. Moreover, in this work, we also present results for instance-level segmentation using ResNet-based DeepLab, a …
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Instruction Mining from Images: Constructing a Synthetic Dataset for Multimodal Learning
… και υψηλής ποιότητας multimodal instruction datasets. Η χειροκίνητη δημιουργία τέτοιων συνόλων δεδομένων είναι ιδιαίτερα δαπανηρή, χρονοβόρα και δύσκολα επεκτάσιμη, ειδικά σε εργασίες που απαιτούν σύνθετο οπτικό συλλογισμό και ακριβές grounding μεταξύ εικόνας και κειμένου. Σκοπός της παρούσας …
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Towards high fidelity mapping of global inland water quality using earth observation data
… fill this gap through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which attempts to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, …
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A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery
… sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.
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DeepMVS: learning multi-view stereopsis
… (1) supervised pretraining on a photorealistic synthetic dataset, (2) an effective method for aggregating information across a set of unordered images, and (3) integrating multi-layer feature activations from the pre-trained VGG-19 network. We validate the efficacy of DeepMVS using the ETH3D …
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Estimating a Baseball Hitter’s Bat Speed Using One Camera
… the success a regression model can have on a synthetic dataset of swings as proof of concept.
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Quake-OVUDA: Component-Level AI-Based Post-Earthquake Building Inspections with Vision–Language Guided Unsupervised Domain Adaptation
… is the manual development of large, annotated datasets for the real world. Training Unsupervised Domain Adaptation (UDA) models, using synthetic, automatically generated imagery, has shown promise in reducing reliance on manual labeling and improving the adaptation of features learned from the …
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Imbalanced learning using actuarial modified loss function in tree-based models
… claim loss modeling. Via an illustrative simple dataset, this thesis first pinpoints the pitfall in the traditional tree-based algorithm’s splitting function. This thesis then modifies the function to remedy the imbalance issue presented in the insurance loss modeling. We propose two novel …
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Counting with convolutional neural networks
… how many are there? To study this, we create a synthetic dataset consisting of black and white images with variable numbers of white triangles on a black background, oriented right-side up, down, left or right. We train a network to count the right-side up triangles; specifically, we see this as …
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Discovery of overlapping 1-closed biclusters
… the edges that may be missing in the original dataset. We have designed a novel algorithm, and tested it using a synthetic and two biomedical datasets from the field of genomics. We predict target disease – gene relationships which are relatively weakly linked as compared to a closed bicluster …
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Utilizing Deep Learning Audio Models for Blind and Low Vision Crosswalk Assistance
… such as EINV2. To train these models, a synthetic dataset of quadraphonic audio from simulated 4-way traffic scenes is generated using Unity Engine, addressing both the scalability and privacy concerns of real-world data collection. This work begins an exploration into a hands-free, …
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Data curation for foundation model training
… we will examine ways to increase the quality of datasets in image-text and language domains, with a focus on dataset curation and filtering. Given the amount of readily available data on the web, these techniques can be reliably applied as methods to increase the downstream performance of models, …
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A study of type-2 fuzzy clustering
… along with the experiments conducted on a synthetic dataset named the butterfly dataset. We also illustrate the updated memberships (fuzzy numbers) and the resulting cluster prototypes (fuzzy vectors) from visual standpoints. Applying any of these dampening approaches will result in thinner …
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Quantifying phytoplankton biomass and sediment in river plumes along the Agulhas Bank using remotely sensed data with deep learning techniques
… satellite data. The MLP model is trained on a synthetic dataset of Rrs parameterised using the in situ ranges of TSM and [Chl-a]. The MLP model was evaluated using the three mentioned ACs as the model requires Rrs uncontaminated by the atmosphere. The regional MLP model was separated into a …
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Optimizing Data Compression via Data Reordering Strategies
… and cost-effectiveness of handling large tabular datasets stored in databases, a range of data compression techniques are employed. Among these, dictionary-based compression methods such as Lz4, Gzip, and Zstandard are commonly utilized to decrease data size. However, while these traditional …
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Inferring properties of neural networks with intelligent designs
… networks reveals information about the training dataset. This could be dire in many scenarios such as network log anomaly classifiers leaking data about the network they were trained on, disease detectors revealing information about participants such as genomic markers, facial recognition …
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Privacy-Preserving Natural Language Dataset Generation
… attacks, we propose the generation of private synthetic datasets to replace the original datasets in training and testing the model. These synthetic datasets will have the same semantic and statistical distribution as the original dataset, but will be differentially private, thus preventing …
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Multi-Level Quantization of Stochastic Variational Inference based Bayesian Neural Networks
… for BNNs. As the main benchmark we utilize a synthetic dataset for a regression task. This dataset specifically allows us to characterize both regression performance and uncertainty prediction of aleatoric and epistemic uncertainty. We first analyze the crucial role of input representation in …
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Continuous Personalized Fall Detection and Data Collection
… fall detection that utilizes only a simulated dataset cannot be recommended for real-world application as it does not reflect the characteristics of fall from the target population. We propose a solution that will move fall detection into the real world by being capable of collecting real data …
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Applying Flinet Deep Learning Model to Fluorescence Lifetime Imaging Microscopy for Lifetime Parameter Prediction
… curve fitting required, especially with large datasets or in noisy conditions. These data may be analyzed by FLINET, a deep learning architecture designed specifically for lifetime parameter prediction. The goal of this study is to train the existing FLINET model on synthetic data that best …
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