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.
Results
Showing 1 to 20 of 472 for “"Convolutional neural networks"”.
-
Counting with convolutional neural networks
In this work, we tackle the question: Can neural networks count? More precisely, given an input image with a certain number of objects, can a neural network tell how many are there? To study this, we create a synthetic dataset consisting of black and white images with variable numbers of white …
-
Visualization of Deep Convolutional Neural Networks
… 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 classification result is achieved. The objective of this thesis is to explore several …
-
Classifying GPR images using convolutional neural networks
… medium using four different architectures of convolutional neural networks. Two CNNs were newly proposed for this study, while the other two were used by other authors. These CNNs were trained using a couple of adjusted training options including initial learning rate, learn rate drop factor, …
-
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 task …
-
Greedy layerwise training of convolutional neural networks
… to end-to-end back-propagation for training deep convolutional neural networks. Although previous work was unsuccessful in demonstrating the viability of layerwise training, especially on large-scale datasets such as ImageNet, recent work has shown that layerwise training on specific architectures …
-
Handling Invalid Pixels in Convolutional Neural Networks
Most neural networks use a normal convolutional layer that assumes that all input pixels are valid pixels. However, pixels added to the input through padding result in adding extra information that was not initially present. This extra information can be considered invalid. Invalid pixels can also …
-
Human action recognition with 3D convolutional neural networks
Convolutional neural networks (CNNs) adapt the regular fully-connected neural network (NN) algorithm to facilitate image classification. Recently, CNNs have been demonstrated to provide superior performance across numerous image classification databases including large natural images (Krizhevsky et …
-
Attributed Graph Classification via Deep Graph Convolutional Neural Networks
From social networks to biological networks, graphs are a natural way to represent a diverse set of real-world data. This research presents attributed graph convolutional neural network with a pooling layer (AGCP for short), a novel end-to-end deep neural network model which captures the …
-
The Removal of False Signals from Convolutional Neural Networks
Convolutional neural networks have achieved performance comparable to human experts in various computer vision tasks. However, despite this comparable performance, the decision-making process differs significantly between convolutional neural networks and human experts. Whereas human decisions are …
-
Vertical Optimizations of Convolutional Neural Networks for Embedded Systems
L'abstract è presente nell'allegato / the abstract is in the attachment
-
Low-complexity convolutional neural networks for automatic target recognition
… there has been growing interest in developing neural network based automatic target recognition systems for synthetic aperture radar applications. However, these networks are typically complex in terms of storage and computation which inhibits their deployment in the field, where such resources …
-
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 …
-
Priors in finite and infinite Bayesian convolutional neural networks
Bayesian neural networks (BNNs) have undergone many changes since the seminal work of Neal [Nea96]. Advances in approximate inference and the use of GPUs have scaled BNNs to larger data sets, and much higher layer and parameter counts. Yet, the priors used for BNN parameters have remained …
-
Radar intra-pulse modulation classification using convolutional neural networks
… techniques - more specifically, by utilising convolutional neural networks. A wide range of modulation schemes was considered and simulated with realistic imperfections to create a dataset that was as representative of real-world scenarios as possible. Data representations of varying levels of …
-
Decoding Invisible 3D Printed Tags with Convolutional Neural Networks
… of parameters. It will instead use convolution neural networks (CNNs) to quickly convert an IR image into a binary image, from which the embedded code can be readily read.
-
Visual tasks beyond categorization for training convolutional neural networks
… category. In this paper, we explore- whether convolutional neural networks (CNNs) can also learn object-related variables. The models are trained for object position, size and pose, respectively, from synthetic images and tested on unseen held-out objects. First, we show that some object …
-
Visualizing and interpreting convolutional neural networks on genomic data
… of the resulting genomic deep learning networks remains challenging. While many network visualization tools focus on directly mapping high level neuron features into input space, they do not explicitly reflect how a network combines these features when making predictions. Moreover, many …
-
A Study on Deepsea Fish Detection Using Convolutional Neural Networks
<p>This study investigates automated deep-learning methodologies for two primary tasks, firstly fish and its habitat classification and deep-sea fish detection using the DeepFish dataset. A pretrained ResNet-50 model attained a validation accuracy of 82.8% for multi-class habitat classification, …
-
Transformation-invariance properties of Grad-CAM in Convolutional Neural Networks
The widespread adoption of Convolutional Neural Networks (CNNs) in image classification tasks has led to increasing interest in interpreting their decisions. Gradient weighted Class Activation Mapping (Grad-CAM) is a post-hoc explanation technique that generates visual heatmaps to highlight the …
Page 1 of 24