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 35 for “"Feature maps"”.
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Principles underlying the development and organization of feature maps in the visual cortex
… principles can explain the layouts of cortical maps, such as those of visual space and of specific response features, remains unresolved. We find that in primary visual cortex of ferret, the layout of each map is inter-dependent with that of the others. First, we find a strong anisotropy in the …
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Lifting 2D Vision Models into Structured Scene Representations
… several works have demonstrated lifting feature maps produced by these models into a 3D feature representation. This thesis further explores how lifting can be effectively employed to construct pixel-level fidelity structured scene representations. Learned scene representations such as …
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Fine-grained artworks classification
… We propose a new architecture that aggregates features from different convolutional layers to exploit earlier layer features. The new architecture is evaluated on the challenging fine-grained artist and year classification. We also propose a regularization method that penalizes correlations of …
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Characterization of Photoacoustic Flow Cytometry Signals
… to allow for further classification. Novel features were extracted from waveforms that appear to have strong classification ability. Neural networks were also used to determine classification potential and the creation of feature mapping for future unsupervised classification. Detections …
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ComBo : a visualization tool for comparative genomic data
… dot plot format, display sequence annotations in feature maps, and compare annotations in corresponding regions. ComBo allows users to directly control the display. They can compare any two regions in the sequences at any resolution, from an entire chromosome down to the residue level. Users can …
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Developing neural network applications using LabVIEW
… NN, Principal Component NN, and Self- Organizing feature maps have been used as tools to develop applications such as pattern classification, image compression and plant modeling in a LabVIEW environment.
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Modeling Cognition by Pruning and Topography-Learning in Deep Neural Networks
… this using structured pruning over learned feature maps or units in convolutional neural networks to select a subset that can improve the alignment with human judgments. This part consists of three studies. In Study 1.1, we introduce a statistic quantifying how much each feature map …
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Stacked dense-hourglass networks for human pose estimation
… later filters the access to all the preceding feature-maps inside the same block. Therefore, dense blocks serve as the upgraded substitution for the residual modules. We evaluate the Stack-DenseHgNets on the popular human pose estimation benchmark dataset and compare its performance to the …
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A Patch-as-Filter Method for Same-Different Problems with Few-Shot Learning
… the original input images, we, lastly, acquire feature maps indicating the duplicate segments. We show experimentally that our approach achieves the state-of-the-art few-shot performance on the Synthetic Visual Reasoning Test (SVRT) SD tasks by accuracy going up above 30% on average, with only …
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Object Detection on Unmanned Arial Vehicles Dataset Using Adaptive HydraNet
… (ABN), which applies AHydraNet to all the output feature maps of the feature pyramid network. The resulting model is called AHydraFPN. The performance of AHydraFPN is evaluated on two popular datasets, MS-COCO and Arial-Cars, and compare it with the performance of Mask R-CNN. Our experimental …
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Visualizing and interpreting convolutional neural networks on genomic data
Deep learning's capability to learn derived features through a hierarchy of non-linear layers has proven superior to other machine learning methods. However, interpretation of the resulting genomic deep learning networks remains challenging. While many network visualization tools focus on directly …
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A comprehensive chemical examination of methylamphetamine produced from pseudoephedrine extracted from cold medication
… and artificial neural networks (self organizing feature maps) were used to investigate the separation of samples to the individual routes and precursor extracted from the individual solvent systems. The mathematical tools demonstrated that methylamphetamine profiling linking precursors sourced …
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Melt Pool Feature Extraction, A State-Space Model and Spatial Layer-to-Layer Control for Powder Bed Fusion
… and melt pool morphology in PBF. With melt pool features from camera videos, manufacturing researchers use spatial feature maps to visualize defect locations and control researchers use layer-to-layer control to achieve more uniform melt pools and less defects.</p> <p>Paper I presents a …
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Dynamic Neural Network for Efficient Video Recognition
… in redundancy in either temporal or spatial feature maps of the models, or both. The amount of redundancy largely depends on the dynamics and events captured in the video. For example, static videos typically have more temporal redundancy, while videos focusing on objects tend to have more …
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Weak-Supervised Deep Learning Methods for the Analysis of Multi-Source Satellite Remote Sensing Images
… multispectral data and minimize the loss in the feature maps and extends this approach to the analysis of hyperspectral images; (iii) a hybrid deep learning architecture with a discrete wavelet transform and attention mechanism to deal with few labeled samples for scene-based classification of …
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Semi-Supervised Transfer Learning for medical images as an alternative to ImageNet Transfer Learning
… of using large amounts of unlabelled data for feature learning is very attractive. In this work, we propose a semi-supervised transfer learning method for training deep learning models for medical imaging. The main idea behind the proposed method is to leverage unlabelled medical image datasets …
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Modal Analysis of Axisymmetric Structures using Zernike Polynomials and Machine Learning
… Zernike Annular Moment Descriptors (ZAMD) as feature maps, supervised learning models, such as decision trees, random forests, and XGBoost, achieve a high classification accuracy, thus eliminating the need for manual intervention. Furthermore, convolutional neural networks (CNNs), trained on …
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A study on the effect of target object size in object detection
… One in particular is due to the resizing of the feature maps within the pooling stage resulting in the loss of the small target object’s features. Moreover, accuracy diminishes as these networks struggle to distinguish between foreground and complex backgrounds such as rough terrain. An …
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Utilizing Computed to mography Image Features to Advance Prediction of Radiation Pneumonitis
… models for RP by considering CT-based image features extracted from the normal lung volume.</p> <p>To accomplish this, a software framework was first built to facilitate CT image feature extraction using multiple image analysis methods. Subsequently, we applied the implemented methods towards …
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Towards Real-World Quantum Machine Learning
… and decouples qubit requirements from input feature dimension. Compared to prevailing QNN baselines, the CFF-QNN reduces both depth and CNOT count by over 50% while achieving strong performance on standard benchmarks (e.g., 91% accuracy on Wisconsin breast cancer and 85% on credit-card …
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