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The manuscripts, the Wagner edition, and the prologue of the Cauallero ZifarOlsen, Marilyn A. January 1900 (has links)
Thesis (Ph. D.)--University of Wisconsin--Madison, 1975. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.
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Comparative Analysis of Convolutional Neural Network (CNN) Architectures for Content RestrictionDaher, Abdulhadi January 2024 (has links)
Ökningen av sociala medieanvändare har introducerat betydande utmaningar i att hantera de stora mängder data som delas, särskilt bilder. Med mer än 63% av världens befolkning som använder sociala medieplattformar, har behovet av effektiv innehållsbegränsning blivit kritiskt. Manuell moderering är inte längre praktisk på grund av den stora mängden innehåll. Denna studie adresserar det kritiska problemet med bildbegränsning genom att utvärdera prestandan hos avancerade bildklassificeringsmodeller, specifikt VGG16 och Inception_v3 konvolutionella neurala nätverk (CNNs). För att möta denna utmaning använder studien CIFAR-10 datasetet, vilket är allmänt känt som ett riktmärkesdataset inom bildklassificeringsforskning. Forskningen innebär att implementera förtränade modeller och genomföra en omfattande jämförelse med olika prestandamått, inklusive noggrannhet, precision, återkallning, F1-poäng, förväxlingsmatris, ROC-kurva och AUC. Dessa mått ger en omfattande utvärdering av modellens förmåga att korrekt klassificera bilder. Vidare inkluderar studien en finjusteringsfas efter den inledande jämförelsen för att ytterligare förbättra modellens prestanda. Detta innebär att justera parametrarna i den förtränade modellen för att bättre passa de specifika egenskaperna hos CIFAR-10 datasetet. Efter finjusteringen genomförs ytterligare en jämförande analys för att bedöma förbättringarna och fastställa den mest effektiva modellen. Resultaten visar att både VGG16 och Inception_V3 visade betydande förbättringar i prestanda efter finjustering, med märkbara ökningar i noggrannhet och andra mått. Emellertid visade VGG16 bättre övergripande prestanda, vilket gör den till den föredragna modellen för denna applikation. Huvudsyftet med denna forskning är att identifiera den mest effektiva modellen för bildklassificering och därigenom etablera ett fundamentalt konceptbevis för användningen av konvolutionella neurala nätverk (CNNs) i innehållsbegränsning på sociala medieplattformar. / The increase in social media usage has introduced significant challenges in managing the large amounts of data being shared, particularly images. With more than 63% of the global population using social media platforms, the need for effective content restriction has become critical. Manual moderation is no longer practical due to the large amount of content. This thesis addresses the critical issue of image restriction by evaluating the performance of advanced image classification models, specifically VGG16 and Inception_v3 Convolutional Neural Networks (CNNs). In order to address this challenge, the study utilizes the CIFAR-10 dataset, which is widely known as a benchmark dataset in image classification research. The research involves implementing pre-trained models and conducting a comprehensive comparison using various performance metrics, including Accuracy, Precision, Recall, F1 Score, Confusion Matrix, ROC Curve, and AUC. These metrics provide a comprehensive evaluation of the model's ability to accurately classify images. Furthermore, the study includes a fine-tuning phase after the initial comparison to further improve the model's performance. This involves adjusting the parameters of the pre-trained model to better suit the specific characteristics of the CIFAR-10 dataset. Following the finetuning, another round of comparative analysis is conducted to assess the improvements and determine the most effective model. The results demonstrate that both VGG16 and Inception_V3 showed significant improvements in performance after fine-tuning, with notable increases in accuracy and other metrics. However, VGG16 showed a better overall performance, making it the preferred model for this application. The primary objective of this research is to identify the most effective model for image classification, thereby establishing a foundational proof of concept for the application of Convolutional Neural Networks (CNNs) in content restriction on social media platforms.
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Convolutional Neural Networks on FPGA and GPU on the Edge: A ComparisonPettersson, Linus January 2020 (has links)
When asked to implement a neural network application, the decision concerning what hardware platform to use may not always be easily made. This thesis studies various relevant platforms with regards to performance, power efficiency and usability, with the purpose of providing a basis for such decisions. The hardware platforms which are studied were a GPU, an FPGA and a CPU. The project implements Convolutional Neural Networks (CNN) on the different hardware platforms using several tools and frameworks. The final implementation uses BNN-PYNQ for the implementation on the FPGA and CPU, which provided ready-to-run code and overlays for quantized CNNs and fully connected neural networks. Next, these networks are copied using TensorFlow, and optimized to FP32, FP16 and INT8 precision using TensorRT for use on the GPU. The results indicate that the FPGA outperforms the GPU with a factor of 100 for the CNN networks, and a factor of 1000 on the fully connected networks with regards to inference speed. For power efficiency, the FPGA again outperforms the GPU. The thesis concludes that for a neural network application, an FPGA is preferred if performance is a priority. However, the GPU proved to have a greater ease of use due to the many tools and frameworks available. If easy implementation and high design flexibility is a priority, a GPU is instead recommended.
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Performance analysis: CNN model on smartphones versus on cloud : With focus on accuracy and execution timeKlas, Stegmayr, Edwin, Johansson January 2023 (has links)
In the modern digital landscape, mobile devices serve as crucial data generators.Their usage spans from simple communication to various applications such as userbehavior analysis and intelligent applications. However, privacy concerns associatedwith data collection are persistent. Deep learning technologies, specifically Convo-lutional Neural Networks, have been increasingly integrated into mobile applicationsas a promising solution. In this study, we evaluated the performance of a CNN im-plemented on iOS smartphones using the CIFAR-10 data set, comparing the model’saccuracy and execution time before and after conversion for on-device deployment.The overarching objective was not to design the most accurate model but to inves-tigate the feasibility of deploying machine learning models on-device while retain-ing their accuracy. The results revealed that both on-cloud and on-device modelsyielded high accuracy (93.3% and 93.25%, respectively). However, a significantdifference was observed in the total execution time, with the on-device model re-quiring a considerably longer duration (45.64 seconds) than the cloud-based model(4.55 seconds). This study provides insights into the performance of deep learningmodels on iOS smartphones, aiding in understanding their practical applications andlimitations.
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A COMPARATIVE STUDY OF FFN AND CNN WITHIN IMAGE RECOGNITION : The effects of training and accuracy of different artificial neural network designsKnutsson, Magnus, Lindahl, Linus January 2019 (has links)
Image recognition and -classification is becoming more important as the need to be able to process large amounts of images is becoming more common. The aim of this thesis is to compare two types of artificial neural networks, FeedForward Network and Convolutional Neural Network, to see how these compare when performing the task of image recognition. Six models of each type of neural network was created that differed in terms of width, depth and which activation function they used in order to learn. This enabled the experiment to also see if these parameters had any effect on the rate which a network learn and how the network design affected the validation accuracy of the models. The models were implemented using the API Keras, and trained and tested using the dataset CIFAR-10. The results showed that within the scope of this experiment the CNN models were always preferable as they achieved a statistically higher validation accuracy compared to their FFN counterparts.
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Classification of road side material using convolutional neural network and a proposed implementation of the network through Zedboard Zynq 7000 FPGARahman, Tanvir 12 1900 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / In recent years, Convolutional Neural Networks (CNNs) have become the state-of-
the-art method for object detection and classi cation in the eld of machine learning
and arti cial intelligence. In contrast to a fully connected network, each neuron of a
convolutional layer of a CNN is connected to fewer selected neurons from the previous
layers and kernels of a CNN share same weights and biases across the same input layer
dimension. These features allow CNN architectures to have fewer parameters which in
turn reduces calculation complexity and allows the network to be implemented in low
power hardware. The accuracy of a CNN depends mostly on the number of images
used to train the network, which requires a hundred thousand to a million images.
Therefore, a reduced training alternative called transfer learning is used, which takes
advantage of features from a pre-trained network and applies these features to the new
problem of interest. This research has successfully developed a new CNN based on
the pre-trained CIFAR-10 network and has used transfer learning on a new problem
to classify road edges. Two network sizes were tested: 32 and 16 Neuron inputs with
239 labeled Google street view images on a single CPU. The result of the training
gives 52.8% and 35.2% accuracy respectively for 250 test images. In the second part
of the research, High Level Synthesis (HLS) hardware model of the network with 16
Neuron inputs is created for the Zynq 7000 FPGA. The resulting circuit has 34%
average FPGA utilization and 2.47 Watt power consumption. Recommendations to
improve the classi cation accuracy with deeper network and ways to t the improved
network on the FPGA are also mentioned at the end of the work.
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Increasing CNN representational power using absolute cosine value regularizationSingleton, William S. 05 1900 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / The Convolutional Neural Network (CNN) is a mathematical model designed to distill input information into a more useful representation. This distillation process removes information over time through a series of dimensionality reductions, which ultimately, grant the model the ability to resist noise, and generalize effectively. However, CNNs often contain elements that are ineffective at contributing towards useful representations. This Thesis aims at providing a remedy for this problem by introducing Absolute Cosine Value Regularization (ACVR). This is a regularization technique hypothesized to increase the representational power of CNNs by using a Gradient Descent Orthogonalization algorithm to force the vectors that constitute their filters at any given convolutional layer to occupy unique positions in in their respective spaces. This method should in theory, lead to a more effective balance between information loss and representational power, ultimately, increasing network performance. The following Thesis proposes and examines the mathematics and intuition behind ACVR, and goes on to propose Dynamic-ACVR (D-ACVR). This Thesis also proposes and examines the effects of ACVR on the filters of a low-dimensional CNN, as well as the effects of ACVR and D-ACVR on traditional Convolutional filters in VGG-19. Finally, this Thesis proposes and examines regularization of the Pointwise filters in MobileNetv1.
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Pruning Convolution Neural Network (SqueezeNet) for Efficient Hardware DeploymentAkash Gaikwad (5931047) 17 January 2019 (has links)
<p>In recent years, deep learning models have become popular in
the real-time embedded application, but there are many complexities for
hardware deployment because of limited resources such as memory, computational
power, and energy. Recent research in the field of deep learning focuses on
reducing the model size of the Convolution Neural Network (CNN) by various
compression techniques like Architectural compression, Pruning, Quantization,
and Encoding (e.g., Huffman encoding). Network pruning is one of the promising
technique to solve these problems.</p>
<p>This thesis proposes methods to
prune the convolution neural network (SqueezeNet) without introducing network
sparsity in the pruned model. </p>
<p>This thesis proposes three methods to prune the CNN to
decrease the model size of CNN without a significant drop in the accuracy of
the model.</p>
<p>1: Pruning based on Taylor expansion of change in cost
function Delta C.</p>
<p>2: Pruning based on L<sub>2</sub> normalization of activation maps.</p>
<p>3: Pruning based on a combination of method 1 and method 2.</p><p>The proposed methods use various
ranking methods to rank the convolution kernels and prune the lower ranked
filters afterwards SqueezeNet model is fine-tuned by backpropagation. Transfer
learning technique is used to train the SqueezeNet on the CIFAR-10 dataset.
Results show that the proposed approach reduces the SqueezeNet model by 72%
without a significant drop in the accuracy of the model (optimal pruning
efficiency result). Results also show that Pruning based on a combination of
Taylor expansion of the cost function and L<sub>2</sub> normalization of activation maps
achieves better pruning efficiency compared to other individual pruning
criteria and most of the pruned kernels are from mid and high-level layers. The
Pruned model is deployed on BlueBox 2.0 using RTMaps software and model
performance was evaluated.</p><p></p>
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Increasing CNN Representational Power Using Absolute Cosine Value RegularizationWilliam Steven Singleton (8740647) 21 April 2020 (has links)
The Convolutional Neural Network (CNN) is a mathematical model designed to distill input information into a more useful representation. This distillation process removes information over time through a series of dimensionality reductions, which ultimately, grant the model the ability to resist noise, and generalize effectively. However, CNNs often contain elements that are ineffective at contributing towards useful representations. This Thesis aims at providing a remedy for this problem by introducing Absolute Cosine Value Regularization (ACVR). This is a regularization technique hypothesized to increase the representational power of CNNs by using a Gradient Descent Orthogonalization algorithm to force the vectors that constitute their filters at any given convolutional layer to occupy unique positions in R<sup>n</sup>. This method should in theory, lead to a more effective balance between information loss and representational power, ultimately, increasing network performance. The following Thesis proposes and examines the mathematics and intuition behind ACVR, and goes on to propose Dynamic-ACVR (D-ACVR). This Thesis also proposes and examines the effects of ACVR on the filters of a low-dimensional CNN, as well as the effects of ACVR and D-ACVR on traditional Convolutional filters in VGG-19. Finally, this Thesis proposes and examines regularization of the Pointwise filters in MobileNetv1.
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Hyperparameters relationship to the test accuracy of a convolutional neural networkLundh, Felix, Barta, Oscar January 2021 (has links)
Machine learning for image classification is a hot topic and it is increasing in popularity. Therefore the aim of this study is to provide a better understanding of convolutional neural network hyperparameters by comparing the test accuracy of convolutional neural network models with different hyperparameter value configurations. The focus of this study is to see whether there is an influence in the learning process depending on which hyperparameter values were used. For conducting the experiments convolutional neural network models were developed using the programming language Python utilizing the library Keras. The dataset used for this study iscifar-10, it includes 60000 colour images of 10 categories ranging from man-made objects to different animal species. Grid search is used for instantiating models with varying learning rate and momentum, width and depth values. Learning rate is only tested combined with momentum and width is only tested combined with depth. Activation functions, convolutional layers and batch size are tested individually. Grid search is compared against Bayesian optimization to see which technique will find the most optimized learning rate and momentum values. Results illustrate that the impact different hyperparameters have on the overall test accuracy varies. Learning rate and momentum affects the test accuracy greatly, however suboptimal values for learning rate and momentum can decrease the test accuracy severely. Activation function, width and depth, convolutional layer and batch size have a lesser impact on test accuracy. Regarding Bayesian optimization compared to grid search, results show that Bayesian optimization will not necessarily find more optimal hyperparameter values.
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