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Dissertations / Theses on the topic 'Convolutive Neural Networks'

Author: Grafiati

Published: 25 May 2024

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1

Heuillet, Alexandre. "Exploring deep neural network differentiable architecture design." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG069.

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L'intelligence artificielle (IA) a gagné en popularité ces dernières années, principalement en raison de ses applications réussies dans divers domaines tels que l'analyse de données textuelles, la vision par ordinateur et le traitement audio. La résurgence des techniques d'apprentissage profond a joué un rôle central dans ce succès. L'article révolutionnaire de Krizhevsky et al., AlexNet, a réduit l'écart entre les performances humaines et celles des machines dans les tâches de classification d'images. Des articles ultérieurs tels que Xception et ResNet ont encore renforcé l'apprentissage prof

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Maragno, Alessandro. "Programmazione di Convolutional Neural Networks orientata all'accelerazione su FPGA." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12476/.

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Attualmente la Computer Vision, disciplina che consente di estrarre informazioni a partire da immagini digitali, è uno dei settori informatici più in fermento. Grazie alle recenti conquiste e progressi, tale settore ha raggiunto uno stato di maturità tale da poter essere applicato in svariati ambiti, a partire da quello industriale, fino ad arrivare ad applicazioni più vicine alla vita quotidiana. In particolare, si è raggiunto uno stato dell'arte sempre più solido nel campo del riconoscimento di oggetti (object detection) grazie allo sviluppo delle Convolutional Neural Networks (CNN): sistemi

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Abbasi, Mahdieh. "Toward robust deep neural networks." Doctoral thesis, Université Laval, 2020. http://hdl.handle.net/20.500.11794/67766.

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Dans cette thèse, notre objectif est de développer des modèles d’apprentissage robustes et fiables mais précis, en particulier les Convolutional Neural Network (CNN), en présence des exemples anomalies, comme des exemples adversaires et d’échantillons hors distribution –Out-of-Distribution (OOD). Comme la première contribution, nous proposons d’estimer la confiance calibrée pour les exemples adversaires en encourageant la diversité dans un ensemble des CNNs. À cette fin, nous concevons un ensemble de spécialistes diversifiés avec un mécanisme de vote simple et efficace en termes de calcul pour

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Kapoor, Rishika. "Malaria Detection Using Deep Convolution Neural Network." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613749143868579.

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Yu, Xiafei. "Wide Activated Separate 3D Convolution for Video Super-Resolution." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39974.

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Video super-resolution (VSR) aims to recover a realistic high-resolution (HR) frame from its corresponding center low-resolution (LR) frame and several neighbouring supporting frames. The neighbouring supporting LR frames can provide extra information to help recover the HR frame. However, these frames are not aligned with the center frame due to the motion of objects. Recently, many video super-resolution methods based on deep learning have been proposed with the rapid development of neural networks. Most of these methods utilize motion estimation and compensation models as preprocessing to

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Messou, Ehounoud Joseph Christopher. "Handling Invalid Pixels in Convolutional Neural Networks." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98619.

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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 be inside the image where they are referred to as holes in completion tasks like image inpainting. In this work, we look for a method that can handle both types of invalid pixels. We compare on the same test bench two methods previously used to handle invalid pixels outside the image (Partial and Edg

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Ngo, Kalle. "FPGA Hardware Acceleration of Inception Style Parameter Reduced Convolution Neural Networks." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205026.

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Some researchers have noted that the growth rate in the number of network parameters of many recently proposed state-of-the-art CNN topologies is placing unrealistic demands on hardware resources and limits the practical applications of Neural Networks. This is particularly apparent when considering many of the projected applications (IoT, autonomous vehicles, etc) utilize embedded systems with even greater restrictions on computation and memory bandwidth than the typical research-class computer cluster that the CNN was designed on. The GoogLeNet CNN in 2014 proposed a new level of organizatio

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Pappone, Francesco. "Graph neural networks: theory and applications." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23893/.

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Le reti neurali artificiali hanno visto, negli ultimi anni, una crescita vertiginosa nelle loro applicazioni e nelle architetture dei modelli impiegati. In questa tesi introduciamo le reti neurali su domini euclidei, in particolare mostrando l’importanza dell’equivarianza di traslazione nelle reti convoluzionali, e introduciamo, per analogia, un’estensione della convoluzione a dati strutturati come grafi. Inoltre presentiamo le architetture dei principali Graph Neural Network ed esponiamo, per ognuna delle tre architetture proposte (Spectral graph Convolutional Network, Graph Co

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Sung, Wei-Hong. "Investigating minimal Convolution Neural Networks (CNNs) for realtime embedded eye feature detection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281338.

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With the rapid rise of neural networks, many tasks that used to be difficult to complete in traditional methods can now be solved well, especially in the computer vision field. However, as the tasks we have to solve have become more and more complex, the neural networks we use are becoming deeper and larger. Therefore, although some embedded systems are powerful nowadays, most embedded systems still suffer from memory and computation limitations, which means it is hard to deploy our large neural networks on these embedded devices. This project aims to explore different methods to compress the

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Wu, Jindong. "Pooling strategies for graph convolution neural networks and their effect on classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288953.

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With the development of graph neural networks, this novel neural network has been applied in a broader and broader range of fields. One of the thorny problems researchers face in this field is selecting suitable pooling methods for a specific research task from various existing pooling methods. In this work, based on the existing mainstream graph pooling methods, we develop a benchmark neural network framework that can be used to compare these different graph pooling methods. By using the framework, we compare four mainstream graph pooling methods and explore their characteristics. Furthermore

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GIACOPELLI, Giuseppe. "An Original Convolution Model to analyze Graph Network Distribution Features." Doctoral thesis, Università degli Studi di Palermo, 2022. https://hdl.handle.net/10447/553177.

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Modern Graph Theory is a newly emerging field that involves all of those approaches that study graphs differently from Classic Graph Theory. The main difference between Classic and Modern Graph Theory regards the analysis and the use of graph's structures (micro/macro). The former aims to solve tasks hosted on graph nodes, most of the time with no insight into the global graph structure, the latter aims to analyze and discover the most salient features characterizing a whole network of each graph, like degree distributions, hubs, clustering coefficient and network motifs. The activities carrie

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Ioannou, Yani Andrew. "Structural priors in deep neural networks." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/278976.

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Deep learning has in recent years come to dominate the previously separate fields of research in machine learning, computer vision, natural language understanding and speech recognition. Despite breakthroughs in training deep networks, there remains a lack of understanding of both the optimization and structure of deep networks. The approach advocated by many researchers in the field has been to train monolithic networks with excess complexity, and strong regularization --- an approach that leaves much to desire in efficiency. Instead we propose that carefully designing networks in considerati

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Jackman, Simeon. "Football Shot Detection using Convolutional Neural Networks." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157438.

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In this thesis, three different neural network architectures are investigated to detect the action of a shot within a football game using video data. The first architecture uses con- ventional convolution and pooling layers as feature extraction. It acts as a baseline and gives insight into the challenges faced during shot detection. The second architecture uses a pre-trained feature extractor. The last architecture uses three-dimensional convolution. All these networks are trained using short video clips extracted from football game video streams. Apart from investigating network architecture

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Cranston, Daniel, and Filip Skarfelt. "Normalized Convolution Network and Dataset Generation for Refining Stereo Disparity Maps." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158449.

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Finding disparity maps between stereo images is a well studied topic within computer vision. While both classical and machine learning approaches exist in the literature, they frequently struggle to correctly solve the disparity in regions with low texture, sharp edges or occlusions. Finding approximate solutions to these problem areas is frequently referred to as disparity refinement, and is usually carried out separately after an initial disparity map has been generated. In the recent literature, the use of Normalized Convolution in Convolutional Neural Networks have shown remarkable results

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Highlander, Tyler. "Efficient Training of Small Kernel Convolutional Neural Networks using Fast Fourier Transform." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1432747175.

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Sunesson, Albin. "Establishing Effective Techniques for Increasing Deep Neural Networks Inference Speed." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213833.

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Recent trend in deep learning research is to build ever more deep networks (i.e. increase the number of layers) to solve real world classification/optimization problems. This introduces challenges for applications with a latency dependence. The problem arises from the amount of computations that needs to be performed for each evaluation. This is addressed by reducing inference speed. In this study we analyze two different methods for speeding up the evaluation of deep neural networks. The first method reduces the number of weights in a convolutional layer by decomposing its convolutional kerne

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Shuvo, Md Kamruzzaman. "Hardware Efficient Deep Neural Network Implementation on FPGA." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2792.

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In recent years, there has been a significant push to implement Deep Neural Networks (DNNs) on edge devices, which requires power and hardware efficient circuits to carry out the intensive matrix-vector multiplication (MVM) operations. This work presents hardware efficient MVM implementation techniques using bit-serial arithmetic and a novel MSB first computation circuit. The proposed designs take advantage of the pre-trained network weight parameters, which are already known in the design stage. Thus, the partial computation results can be pre-computed and stored into look-up tables. Then the

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Andersson, Viktor. "Semantic Segmentation : Using Convolutional Neural Networks and Sparse dictionaries." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139367.

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The two main bottlenecks using deep neural networks are data dependency and training time. This thesis proposes a novel method for weight initialization of the convolutional layers in a convolutional neural network. This thesis introduces the usage of sparse dictionaries. A sparse dictionary optimized on domain specific data can be seen as a set of intelligent feature extracting filters. This thesis investigates the effect of using such filters as kernels in the convolutional layers in the neural network. How do they affect the training time and final performance? The dataset used here is the

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Bereczki, Márk. "Graph Neural Networks for Article Recommendation based on Implicit User Feedback and Content." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300092.

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Recommender systems are widely used in websites and applications to help users find relevant content based on their interests. Graph neural networks achieved state- of-the- art results in the field of recommender systems, working on data represented in the form of a graph. However, most graph- based solutions hold challenges regarding computational complexity or the ability to generalize to new users. Therefore, we propose a novel graph- based recommender system, by modifying Simple Graph Convolution, an approach for efficient graph node classification, and add the capability of generalizing t

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Schembri, Massimo. "Anomaly Prediction in Production Supercomputer with Convolution and Semi-supervised autoencoder." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22379/.

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Un sistema HPC (High Performance Computing) è un sistema con capacità computazionali molto elevate adatto a task molto esigenti in termini di risorse. Alcune delle proprietà fondamentali di un sistema del genere sono certamente la disponibilità e l'affidabilità che possono essere messe a rischio da problemi hardware e software. In quest'attività di tesi si è realizzato e analizzato le performance di un sistema di anomaly detection in termini di capacità di rilevazione e predizione di un'anomalia su vari nodi di un sistema HPC, in particolare utilizzando i dati relativi al sistema MARCONI del c

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Lamouret, Marie. "Traitement automatisés des données acoustiques issues de sondeurs multifaisceaux pour la cartographie des fonds marins." Electronic Thesis or Diss., Toulon, 2022. http://www.theses.fr/2022TOUL0002.

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Le sondeur multifaisceaux (SMF) est l'une des technologies d'acoustique sous-marine les plus avancées pour l'étude des fonds et de la colonne d'eau. Il requiert une réelle expertise pour son déploiement sur le terrain ainsi que pour l'élaboration de cartographies à partir des différentes données acquises. Ces traitements sont souvent chronophages en raison de la quantité de données acquises et demandent à être automatisés pour alléger le travail à l'hydrographe. C'est ce sur quoi portent les travaux réalisés durant cette thèse. Après des rappels sur des notions d'acoustique sous-marine, le fon

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Belharbi, Soufiane. "Neural networks regularization through representation learning." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMIR10/document.

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Les modèles de réseaux de neurones et en particulier les modèles profonds sont aujourd'hui l'un des modèles à l'état de l'art en apprentissage automatique et ses applications. Les réseaux de neurones profonds récents possèdent de nombreuses couches cachées ce qui augmente significativement le nombre total de paramètres. L'apprentissage de ce genre de modèles nécessite donc un grand nombre d'exemples étiquetés, qui ne sont pas toujours disponibles en pratique. Le sur-apprentissage est un des problèmes fondamentaux des réseaux de neurones, qui se produit lorsque le modèle apprend par coeur les d

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Karimi, Ahmad Maroof. "DATA SCIENCE AND MACHINE LEARNING TO PREDICT DEGRADATION AND POWER OF PHOTOVOLTAIC SYSTEMS: CONVOLUTIONAL AND SPATIOTEMPORAL GRAPH NEURAL NETWORK." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1601082841477951.

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Mocko, Štefan. "Využitie pokročilých segmentačných metód pre obrazy z TEM mikroskopov." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-378145.

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Tato magisterská práce se zabývá využitím konvolučních neuronových sítí pro segmentační účely v oblasti transmisní elektronové mikroskopie. Také popisuje zvolenou topologii neuronové sítě - U-NET, použíté augmentační techniky a programové prostředí. Firma Thermo Fisher Scientific (dříve FEI Czech Republic s.r.o) poskytla obrazová data pro účely této práce. Získané segmentační výsledky jsou prezentovány ve formě křivek (ROC, PRC) a ve formě numerických hodnot (ARI, DSC, Chybová matice). Zvolená UNET topologie dosáhla excelentních výsledků v oblasti pixelové segmentace. S největší pravděpodobnos

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Elavarthi, Pradyumna. "Semantic Segmentation of RGB images for feature extraction in Real Time." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573575765136448.

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Lamma, Tommaso. "A mathematical introduction to geometric deep learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23886/.

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Lo scopo del geometric deep learning è quello di estendere l'algoritmo di deep learning sviluppato per la classificazione di immagini a domini non euclidei come grafi e complessi simpliciali.In questa tesi ci proponiamo di dare una definizione matematica dei concetti cardine utilizzati nel geometric deep learning quali equivarianza e convoluzione sui grafi. Vedremo inoltre come definire una rete convoluzionale invariante rispetto all'azione di gruppi.

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Ďuriš, Denis. "Detekce ohně a kouře z obrazového signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-412968.

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This diploma thesis deals with the detection of fire and smoke from the image signal. The approach of this work uses a combination of convolutional and recurrent neural network. Machine learning models created in this work contain inception modules and blocks of long short-term memory. The research part describes selected models of machine learning used in solving the problem of fire detection in static and dynamic image data. As part of the solution, a data set containing videos and still images used to train the designed neural networks was created. The results of this approach are evaluated

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Sparr, Henrik. "Object detection for a robotic lawn mower with neural network trained on automatically collected data." Thesis, Uppsala universitet, Datorteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444627.

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Machine vision is hot research topic with findings being published at a high pace and more and more companies currently developing automated vehicles. Robotic lawn mowers are also increasing in popularity but most mowers still use relatively simple methods for cutting the lawn. No previous work has been published on machine learning networks that improved between cutting sessions by automatically collecting data and then used it for training. A data acquisition pipeline and neural network architecture that could help the mower in avoiding collision was therefor developed. Nine neural networks

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Trčka, Jan. "Zlepšování kvality digitalizovaných textových dokumentů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417278.

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The aim of this work is to increase the accuracy of the transcription of text documents. This work is mainly focused on texts printed on degraded materials such as newspapers or old books. To solve this problem, the current method and problems associated with text recognition are analyzed. Based on the acquired knowledge, the implemented method based on GAN network architecture is chosen. Experiments are a performer on these networks in order to find their appropriate size and their learning parameters. Subsequently, testing is performed to compare different learning methods and compare their

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Oquab, Maxime. "Convolutional neural networks : towards less supervision for visual recognition." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE061.

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Les réseaux de neurones à convolution sont des algorithmes d’apprentissage flexibles qui tirent efficacement parti des importantes masses de données qui leur sont fournies pour l’entraînement. Malgré leur utilisation dans des applications industrielles dès les années 90, ces algorithmes n’ont pas été utilisés pour la reconnaissance d’image à cause de leurs faibles performances avec les images naturelles. C’est finalement grâce a l’apparition d’importantes quantités de données et de puissance de calcul que ces algorithmes ont pu révéler leur réel potentiel lors de la compétition ImageNet, menan

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Vančo, Timotej. "Self-supervised učení v aplikacích počítačového vidění." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442510.

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The aim of the diploma thesis is to make research of the self-supervised learning in computer vision applications, then to choose a suitable test task with an extensive data set, apply self-supervised methods and evaluate. The theoretical part of the work is focused on the description of methods in computer vision, a detailed description of neural and convolution networks and an extensive explanation and division of self-supervised methods. Conclusion of the theoretical part is devoted to practical applications of the Self-supervised methods in practice. The practical part of the diploma thesi

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Tiensuu, Jacob, Maja Linderholm, Sofia Dreborg, and Fredrik Örn. "Detecting exoplanets with machine learning : A comparative study between convolutional neural networks and support vector machines." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385690.

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In this project two machine learning methods, Support Vector Machine, SVM, and Convolutional Neural Network, CNN, are studied to determine which method performs best on a labeled data set containing time series of light intensity from extrasolar stars. The main difficulty is that in the data set there are a lot more non exoplanet stars than there are stars with orbiting exoplanets. This is causing a so called imbalanced data set which in this case is improved by i.e. mirroring the curves of stars with an orbiting exoplanet and adding them to the set. Trying to improve the results further, some

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HSIEH, PO-FENG, and 謝柏鋒. "Visualization of Convolution Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/qna29g.

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碩士<br>國立臺北科技大學<br>資訊工程系<br>107<br>In recent years, convolutional neural networks have had many groundbreaking developments. This paper's goal is to analyze the recent YOLO (You Only Look Once) that has a very good performance classification for object detection technology. This paper is a simple way to explain the operation of the convolutional neural network. Present the process which can make the general public more aware of the way machine learning works, and also make it convenient for experts to analyze the structure of it. The ability to quickly improve the original architecture and acce

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Checg, Chung-Sheng, and 鄭仲勝. "An Accelerative Convolution Neural Network Model." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/2tycsb.

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碩士<br>國立臺北科技大學<br>自動化科技研究所<br>106<br>Machine learning is a technology that allows computers to learn the rules through vast amounts of information and correct their mistakes themselves. It show the superiority to conventional artificial methods. However in shallow learning, the capability of modelling complex functions is limited in the case of finite samples. Thus, shallow learning models are not enough to simulate human brains in solving difficult problems. Until recently, deep learning was proposed to model complex functions that shallow learning cannot achieve and automatically extract dat

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KANG, NAN-RAN, and 康乃人. "Speaker Verification using Convolution Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/c8e3qe.

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碩士<br>逢甲大學<br>資訊工程學系<br>106<br>Biometric system is no longer a new thing in daily life, and it has become more and more popular in recent years, fingerprint recognition, iris recognition, voiceprint recognition, and I-phone's Face ID are all biometric system, and speaker verification is one of them. Speaker recognition can be divided into two parts: feature extraction and classification. In the past, the two parts were solved by different methods, due to the rapid development of deep learning, the neural network for speaker recognition has gained breadth of development. In the part of the spea

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Sun, Tzu-Chun, and 孫梓鈞. "Fruit Recognition Using Deep Convolution Neural Network." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/76315586416634324332.

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碩士<br>國立暨南國際大學<br>資訊工程學系<br>102<br>This thesis focuses on developing a fruit recognition method. It can be used to improve life convenience by shortening the supermarket checkout time. Existing methods for fruit recognition use handcrafted image features, such as the texture, the color, and the shape of a fruit, for fruit recognition. However, image features extracted with a set of specific algorithms do not necessarily provide enough information for pattern recognition. In this work, we use deep convolution neural network (DCNN) to learn discriminative fruit features automatically. In order

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WEI, TSUNG-HSIN, and 魏崇訓. "Video Super-resolution via Convolution Neural Network." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/823kfa.

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碩士<br>國立高雄應用科技大學<br>資訊工程系<br>104<br>Nowadays, people might need super resolution to have more effective and clear information. The technology of image processing becomes better and better, and there are more and more people present their research in this field. Super resolution algorithm enhances high frequent information (texture or edges) to improve the image quality. We can do more things with super resolution, such as road surveillance system. The view might be influence by illumination, angle, distance, and other conditions, so these might not be good for us to recognize the number of lic

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Chang, Yao-Ren, and 張耀仁. "Convolution neural network on WIFI indoor localization." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/s7dhny.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>106<br>The mobile payment has been growing very quickly in these year, our life has become more and more convenient. Once we can locate user’s position precisely, we can broadcast the advertisement to the user to increase sales performance. For example: when you walk into the restaurant, the system sent you the coupon of this restaurant immediately, when you walk into the apparel store, the system list all of the clothes you might like, when you are leaving parking lot, the system auto-debiting your parking fee. In the past, WIFI localization system is based on RFID

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HUANG, JIAN-JHIH, and 黃健智. "Apply Convolution Neural Network on Vehicle Detection." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4g44kf.

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碩士<br>國立彰化師範大學<br>電機工程學系<br>107<br>With rapid development of computer hardware and software technology, complex image processing and recognition can be performed by computer in recent years. The traditional image recognition has some issues of lack of flexibility and poor accuracy. These disadvantages have improved by the neural network. The earlier multi-layer perceptron is widely applied in various areas, however, its hidden layers are simple and slower convergence issues to cause longer training time. Most researchers apply R-CNN to the real time recognition system. These methods have highe

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Hu, Yi-Chun, and 胡依淳. "Analysis and Comparison of Convolution Layer in Deep Convolution Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/686x7u.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>106<br>With the rapid development of information technology, big data has become mainstream, and many identification systems have been greatly affected. Therefore, deep learning requires a large database learning model and thus becomes the mainstream. Deep learning can take advantage of the characteristics of robots to automatically learn to task objectives, and thus deep learning of this architecture has become a very popular technology in academics. Nowadays, neural networks are popular in the field of visual imaging. The best performing model is the convolutional

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XIE, YOU-TING, and 謝侑廷. "CT Images segmentation using Deep Convolution Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4xkkpc.

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碩士<br>國立臺灣科技大學<br>電子工程系<br>107<br>Cause of hospital unable to provide a complete 3D model and nowadays technol- ogy can not produce stereoscopic images without a blind vision, it cause doctors misjudgment on surgery evaluated or diagnosed. Therefoe doctors can only rely on their own experience to identify computerized tomography (CT) images during diag- nosis and preoperative evaluation. However, images are a kind of two-dimensional information expression, it cannot provide doctors accurate informations in three- dimensional space. With professional training and extensive experience in the eld

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Vijayan, Raghavendran. "Forecasting retweet count during elections using graph convolution neural networks." Thesis, 2018. https://doi.org/10.7912/C2JM2C.

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GUAN, HONG-CHENG, and 管宏成. "Detection of Scooters in Taiwan Based on Convolution Neural Networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/54627521654968631599.

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碩士<br>逢甲大學<br>資訊工程學系<br>105<br>Due to the attention of modern people on the increasing importance of traffic safety, drivers in Taiwan install driving recorder which record daily traffic or accident. Taiwanese usually use scooter as commuter vehicle. Riders in Taiwan are often shuttle in the traffic jam, it is very dangerous for car driver to detect the riders. It is usually to cause car accidents. In this paper, we propose a system to remind car drivers to avoid the danger of rider in the traffic jam or street .The system will draw rectangles on the driving recorder to mark the interested obj

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Cai, Pei Yun, and 蔡佩妘. "Design of A Flexible Accelerator for Deep Convolution Neural Networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/76s688.

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碩士<br>國立清華大學<br>資訊工程學系<br>105<br>Convolution Neural Network (CNN) is a deep learning method for vision recognition. The state-of-the-art accuracy makes it widely used in artificial intelligence, computer vision, self-driving car, etc. However, CNNs are highly computational complex and demand high memory bandwidth. Although we exploit highly parallel computation to achieve effective throughput, the good orchestration of data movements should be taken into consideration to reduce increased memory bandwidth. To address these problems, we present a specialized dataflow with spatial hardware (exten

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Ho, Pin-Hui, and 何品蕙. "Design of an Inference Accelerator for Compressed Convolution Neural Networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/bxwfdn.

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碩士<br>國立清華大學<br>資訊工程學系所<br>106<br>The state-of-the-art convolution neural network (CNNs) are wildly used in the intelligent applications such as the AI systems, nature language processing and image recognition. The huge and growing computation latency of the high-dimensional convolution has become a critical issue. Most of the multiplications in the convolution layers are ineffectual as they involve the multiplication that either one of the input data or both are zero. By pruning the redundant connections in the CNN models and clamping the features to zero by the rectified linear unit (ReLU),

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WANG, WEI-CHUN, and 王薇淳. "Scene Recognition of Remote Sensing Images Using Convolution Neural Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/nd35c3.

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碩士<br>國防大學理工學院<br>空間科學碩士班<br>106<br>Geospatial information often use spatial information and observation system for spatial data processing and analysis to meet the need of applications in different fields. However, in the face of big data satellite images and various types of geospatial data on the Internet, it is hard for users to analyze such huge images and data with great load. In order to effectively conduct image interpretation and earth observation applications, it is no longer possible to work in a traditional manner. In this study, we firstly integrate the remote sensing images and P

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LIU, HONG-CEN, and 劉泓岑. "Combining 1D & 2D Convolution Neural Networks For Fall Detection." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/hk76sd.

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碩士<br>大同大學<br>資訊工程學系(所)<br>107<br>Fall detection mechanism can be sorted by the position where the sensor is placed. One is “Placed in the environment” and the other is “Attached on the human body”. The sensor of the first method usually required to be placed in specific place, and the price of the sensor is expensive. In contrast, the sensor of the second method is not only affordable but also no restrictions on the place. The second method usually uses a smartphone as a sensor, because the smartphone can complete the detection, determination, and notification by itself. The initial research

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Fu, Chien-Chun, and 傅建鈞. "A System for Disguised Face Recognition with Convolution Neural Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/f5gfn3.

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碩士<br>淡江大學<br>電機工程學系碩士在職專班<br>106<br>In this paper, we propose a disguised face recognition system based on Deep Normalization and Convolution Neural Network (DNCNN), this system include two trained DNCNN identification Network. The function of first trained identification network is to identify the type of disguised of the input face image. This network classifies human face disguised input images into three categories, No disguised, Upper half face disguised and Lower half face disguised. After the classification is completed, the system will remove the upper half disguised or the lower half

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(5931047), Akash Gaikwad. "Pruning Convolution Neural Network (SqueezeNet) for Efficient Hardware Deployment." Thesis, 2019.

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<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 pr

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Gaikwad, Akash S. "Pruning Convolution Neural Network (SqueezeNet) for Efficient Hardware Deployment." Thesis, 2018. http://hdl.handle.net/1805/17923.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>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

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