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 51 for “"Deep Convolutional Neural Networks"”.
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Visualization of Deep Convolutional Neural Networks
<p>Deep learning has achieved great accuracy in large scale image classification and scene recognition tasks, especially after the Convolutional Neural Network (CNN) model was introduced. Although a CNN often demonstrates very good classification results, it is usually unclear how or why a …
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Galaxy classification with deep convolutional neural networks
… images are not suitable for galaxy images. Deep convolutional neural networks (CNNs) are able to learn powerful features from images by hierarchical convolutional and pooling operations. This work applies state-of-the-art deep CNN technologies to galaxy classification for both a regression …
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Uses of Complex Wavelets in Deep Convolutional Neural Networks
… in supervised learning methods, particularly convolutional neural networks (CNNs), have pushed forth the frontier of what we have been able to train computers to do. Despite their successes, the mechanics of how these networks are able to recognize objects are little understood, and the …
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Classification of underwater pipeline events using deep convolutional neural networks
… such occurrences. In this work, we present a deep convolutional neural network algorithm for the classification of underwater pipeline events. The neural network architecture and parameters that result in optimal classifier performance are selected. The convolutional neural network technique …
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Detection of Texture-less Occluded Objects Using Deep Convolutional Neural Networks
… easily perform. In order to achieve this task, convolutional neural networks based object detection algorithms have recently opened a new avenue towards the object detection with high precision. The convolutional neural networks require a large amount of training data to train system for …
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Compact and Extended Radio Sources Classification using Deep Convolutional Neural Networks
… the FIRST Classifier based on a trained Deep Convolutional Neural Network Model to automate the morphological classification of compact and extended radio sources observed in the FIRST radio survey. Our model was trained independently for 20 times and achieved an average accuracy, …
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Comparing Learned Representations Between Unpruned and Pruned Deep Convolutional Neural Networks
<p>While deep neural networks have shown impressive performance in computer vision tasks, natural language processing, and other domains, the sizes and inference times of these models can often prevent them from being used on resource-constrained systems. Furthermore, as these networks grow larger …
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A study on deep convolutional neural networks for computer vision applications
… of machine learning based on multiple artificial neural networks. The design of the model of the observation of the functioning of the brain as a set of functional areas that operate in the process of cognition and subsequently use the acquired knowledge in decision-making and execution of complex …
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Deep Convolutional Neural Networks for Classification of Fused Hyperspectral and LiDAR data
Convolutional neural networks (CNN) have demonstrated excellent performance on
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Deep Convolutional Neural Networks Based Single Image Super-Resolution And Classification For Crater Detection
… Based on our HV experiments, we chose Convolutional Neural Networks (CNN) to further investigate how various HG algorithms affect the HV step in order to find an optimal combination for Lunar crater detection.In the second part of this work, we address the problem of small crater …
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Deep Convolutional Neural Networks for Segmenting Unruptured Intracranial Aneurysms from 3D TOF-MRA Images
… overfitting, vanishing and exploding gradients), deep neural networks have the potential to capture complex patterns in data. Understanding how depth impacts neural networks performance is vital to the advancement of novel deep learning architectures. By varying hyperparameters on two sets of …
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AUTOMATIC IDENTIFICATION OF ANIMALS IN THE WILD: A COMPARATIVE STUDY BETWEEN C-CAPSULE NETWORKS AND DEEP CONVOLUTIONAL NEURAL NETWORKS.
… to focus on wild data and keep proving that deep learning can do better and faster than the human equivalent labor for the same task. Moreover, this is also an opportunity to present some custom Capsule Networks architectures to the deep learning community while solving the above-mentioned …
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Fine-grained artworks classification
In this thesis, we apply deep convolutional neural networks to ne-grained artwork classification on the large-scale painting collection, WikiArt. We propose a new architecture that aggregates features from different convolutional layers to exploit earlier layer features. The new architecture is …
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Evaluating ventral visual stream contributions to human visual robustness with deep learning approaches
… abstract representations. In contrast, deep convolutional neural networks (DCNNs), despite being regarded as the closest approximator of the biological visual system, remain vulnerable to variations and adversarial perturbations that humans easily overcome. This discrepancy raises …
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Deep heterogeneous superpixel neural networks for image analysis and feature extraction
Lately, deep convolutional neural networks are rapidly transforming and enhancing computer vision accuracy and performance, and pursuing higher-level and interpretable object recognition. Superpixel-based methodologies have been used in conventional computer vision research where their efficient …
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Texture-based Deep Neural Network for Histopathology Cancer Whole Slide Image (WSI) Classification
… WSIs is time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable texture features from histopathological WSIs. The …
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Expresso-AI : a framework for explainable video based deep learning models through gestures and expressions
… a framework to analyze the decisions of Deep Neural Networks trained on facial videos. We test this framework on Automatic Depression Detection. We first train Deep Convolutional Neural Networks (DCNN) pre-trained on Action Recognition datasets and fine-tune on the facial videos. We …
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Object localization and identification for autonomous operation of surface marine vehicles
… Machine Learning algorithms. In particular, deep Convolutional Neural Networks are first trained offline using a collection of images of possible objects to be encountered (navy ships, sail boats, power boats, buoys, bridges, etc.). The trained network applied to new images returns real-time …
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Detecting basal cell carcinoma in skin histopathological images using deep learning
… of BCC at the patch level, using pre-trained deep convolutional neural networks as feature extractors to compensate for the size of our datasets. The experimental results show that our patch level classifiers obtained an area under the receiver operating characteristic curve (AUC) of 0.981. …
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