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Thèses sur le sujet « Deep Learning in CI »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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1

Dufourq, Emmanuel. "Evolutionary deep learning." Doctoral thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/30357.

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The primary objective of this thesis is to investigate whether evolutionary concepts can improve the performance, speed and convenience of algorithms in various active areas of machine learning research. Deep neural networks are exhibiting an explosion in the number of parameters that need to be trained, as well as the number of permutations of possible network architectures and hyper-parameters. There is little guidance on how to choose these and brute-force experimentation is prohibitively time consuming. We show that evolutionary algorithms can help tame this explosion of freedom, by develo

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He, Fengxiang. "Theoretical Deep Learning." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25674.

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Deep learning has long been criticised as a black-box model for lacking sound theoretical explanation. During the PhD course, I explore and establish theoretical foundations for deep learning. In this thesis, I present my contributions positioned upon existing literature: (1) analysing the generalizability of the neural networks with residual connections via complexity and capacity-based hypothesis complexity measures; (2) modeling stochastic gradient descent (SGD) by stochastic differential equations (SDEs) and their dynamics, and further characterizing the generalizability of deep learning;

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FRACCAROLI, MICHELE. "Explainable Deep Learning." Doctoral thesis, Università degli studi di Ferrara, 2023. https://hdl.handle.net/11392/2503729.

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Il grande successo che il Deep Learning ha ottenuto in ambiti strategici per la nostra società quali l'industria, la difesa, la medicina etc., ha portanto sempre più realtà a investire ed esplorare l'utilizzo di questa tecnologia. Ormai si possono trovare algoritmi di Machine Learning e Deep Learning quasi in ogni ambito della nostra vita. Dai telefoni, agli elettrodomestici intelligenti fino ai veicoli che guidiamo. Quindi si può dire che questa tecnologia pervarsiva è ormai a contatto con le nostre vite e quindi dobbiamo confrontarci con essa. Da questo nasce l’eXplainable Artificial Intelli

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4

Halle, Alex, and Alexander Hasse. "Topologieoptimierung mittels Deep Learning." Technische Universität Chemnitz, 2019. https://monarch.qucosa.de/id/qucosa%3A34343.

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Die Topologieoptimierung ist die Suche einer optimalen Bauteilgeometrie in Abhängigkeit des Einsatzfalls. Für komplexe Probleme kann die Topologieoptimierung aufgrund eines hohen Detailgrades viel Zeit- und Rechenkapazität erfordern. Diese Nachteile der Topologieoptimierung sollen mittels Deep Learning reduziert werden, so dass eine Topologieoptimierung dem Konstrukteur als sekundenschnelle Hilfe dient. Das Deep Learning ist die Erweiterung künstlicher neuronaler Netzwerke, mit denen Muster oder Verhaltensregeln erlernt werden können. So soll die bislang numerisch berechnete Topologieoptimieru

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Goh, Hanlin. "Learning deep visual representations." Paris 6, 2013. http://www.theses.fr/2013PA066356.

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Les avancées récentes en apprentissage profond et en traitement d'image présentent l'opportunité d'unifier ces deux champs de recherche complémentaires pour une meilleure résolution du problème de classification d'images dans des catégories sémantiques. L'apprentissage profond apporte au traitement d'image le pouvoir de représentation nécessaire à l'amélioration des performances des méthodes de classification d'images. Cette thèse propose de nouvelles méthodes d'apprentissage de représentations visuelles profondes pour la résolution de cette tache. L'apprentissage profond a été abordé sous deu

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Geirsson, Gunnlaugur. "Deep learning exotic derivatives." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-430410.

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Monte Carlo methods in derivative pricing are computationally expensive, in particular for evaluating models partial derivatives with regard to inputs. This research proposes the use of deep learning to approximate such valuation models for highly exotic derivatives, using automatic differentiation to evaluate input sensitivities. Deep learning models are trained to approximate Phoenix Autocall valuation using a proprietary model used by Svenska Handelsbanken AB. Models are trained on large datasets of low-accuracy (10^4 simulations) Monte Carlo data, successfully learning the true model with

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Wülfing, Jan [Verfasser], and Martin [Akademischer Betreuer] Riedmiller. "Stable deep reinforcement learning." Freiburg : Universität, 2019. http://d-nb.info/1204826188/34.

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White, Martin. "Deep Learning Software Repositories." W&M ScholarWorks, 2017. https://scholarworks.wm.edu/etd/1516639667.

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Bridging the abstraction gap between artifacts and concepts is the essence of software engineering (SE) research problems. SE researchers regularly use machine learning to bridge this gap, but there are three fundamental issues with traditional applications of machine learning in SE research. Traditional applications are too reliant on labeled data. They are too reliant on human intuition, and they are not capable of learning expressive yet efficient internal representations. Ultimately, SE research needs approaches that can automatically learn representations of massive, heterogeneous, datase

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Sun, Haozhe. "Modularity in deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG090.

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L'objectif de cette thèse est de rendre l'apprentissage profond plus efficace en termes de ressources en appliquant le principe de modularité. La thèse comporte plusieurs contributions principales : une étude de la littérature sur la modularité dans l'apprentissage profond; la conception d'OmniPrint et de Meta-Album, des outils qui facilitent l'étude de la modularité des données; des études de cas examinant les effets de l'apprentissage épisodique, un exemple de modularité des données; un mécanisme d'évaluation modulaire appelé LTU pour évaluer les risques en matière de protection de la vie pr

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Arnold, Ludovic. "Learning Deep Representations : Toward a better new understanding of the deep learning paradigm." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00842447.

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Since 2006, deep learning algorithms which rely on deep architectures with several layers of increasingly complex representations have been able to outperform state-of-the-art methods in several settings. Deep architectures can be very efficient in terms of the number of parameters required to represent complex operations which makes them very appealing to achieve good generalization with small amounts of data. Although training deep architectures has traditionally been considered a difficult problem, a successful approach has been to employ an unsupervised layer-wise pre-training step to init

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Hussein, Ahmed. "Deep learning based approaches for imitation learning." Thesis, Robert Gordon University, 2018. http://hdl.handle.net/10059/3117.

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Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis

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Zhang, Jingwei [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Learning navigation policies with deep reinforcement learning." Freiburg : Universität, 2021. http://d-nb.info/1235325571/34.

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Rodés-Guirao, Lucas. "Deep Learning for Digital Typhoon : Exploring a typhoon satellite image dataset using deep learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249514.

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Efficient early warning systems can help in the management of natural disaster events, by allowing for adequate evacuations and resources administration. Several approaches have been used to implement proper early warning systems, such as simulations or statistical models, which rely on the collection of meteorological data. Data-driven techniques have been proven to be effective to build statistical models, being able to generalise to unseen data. Motivated by this, in this work, we explore deep learning techniques applied to the typhoon meteorological satellite image dataset "Digital Typhoon

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Franceschelli, Giorgio. "Generative Deep Learning and Creativity." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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“Non ha la presunzione di originare nulla; può solo fare ciò che noi sappiamo ordinarle di fare”. Così, oltre 150 anni fa, Lady Lovelace commentava la Macchina Analitica di Babbage, l’antenato dei nostri computer. Una frase che, a distanza di tanti anni, suona quasi come una sfida: grazie alla diffusione delle tecniche di Generative Deep Learning e alle ricerche nell’ambito della Computational Creativity, sempre più sforzi sono stati destinati allo smentire l’ormai celebre Obiezione della Lovelace. Proprio a partire da questa, quattro domande formano i capisaldi della Computational Creativity:

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Shakibi, Babak. "Predicting parameters in deep learning." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50999.

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The recent success of large and deep neural network models has motivated the training of even larger and deeper networks with millions of parameters. Training these models usually requires parallel training methods where communicating large number of parameters becomes one of the main bottlenecks. We show that many deep learning models are over-parameterized and their learned features can be predicted given only a small fraction of their parameters. We then propose a method which exploits this fact during the training to reduce the number of parameters that need to be learned. Our method is or

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Nguyen, Thien Huu. "Deep Learning for Information Extraction." Thesis, New York University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10260911.

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<p> The explosion of data has made it crucial to analyze the data and distill important information effectively and efficiently. A significant part of such data is presented in unstructured and free-text documents. This has prompted the development of the techniques for information extraction that allow computers to automatically extract structured information from the natural free-text data. Information extraction is a branch of natural language processing in artificial intelligence that has a wide range of applications, including question answering, knowledge base population, information ret

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Palasek, Petar. "Action recognition using deep learning." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/30828.

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In this thesis we study deep learning architectures for the problem of human action recognition in image sequences, i.e. the problem of automatically recognizing what people are doing in a given video. As unlabeled video data is easily accessible these days, we first explore models that can learn meaningful representations of sequences without actually having to know what is happening in the sequences at hand. More specifically, we first explore the convolutional restricted Boltzmann machine (RBM) and show how a stack of convolutional RBMs can be used to learn and extract features from sequenc

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Zhuang, Zhongfang. "Deep Learning on Attributed Sequences." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/507.

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Recent research in feature learning has been extended to sequence data, where each instance consists of a sequence of heterogeneous items with a variable length. However, in many real-world applications, the data exists in the form of attributed sequences, which is composed of a set of fixed-size attributes and variable-length sequences with dependencies between them. In the attributed sequence context, feature learning remains challenging due to the dependencies between sequences and their associated attributes. In this dissertation, we focus on analyzing and building deep learning models for

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Zhang, Chiyuan Ph D. Massachusetts Institute of Technology. "Deep learning and structured data." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115643.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 135-150).<br>In the recent years deep learning has witnessed successful applications in many different domains such as visual object recognition, detection and segmentation, automatic speech recognition, natural language proc

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Drexler, Jennifer Fox. "Deep unsupervised learning from speech." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105696.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 87-92).<br>Automatic speech recognition (ASR) systems have become hugely successful in recent years - we have become accustomed to speech interfaces across all kinds of devices. However, despite the huge impact ASR has had on the way we interact with technology, it is out of reach for a significant portion of the world's population. This is because these systems rely on a variety of manually-

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Rippel, Oren. "Sculpting representations for deep learning." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104581.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 149-164).<br>In machine learning, the choice of space in which to represent our data is of vital importance to their effective and efficient analysis. In this thesis, we develop approaches to address a number of problems in representation learning. We employ deep learning as means of sculpting our representations, and also develop improved representations for deep learning models. We present contributions that are based o

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Simonovsky, Martin. "Deep learning on attributed graphs." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1133/document.

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Le graphe est un concept puissant pour la représentation des relations entre des paires d'entités. Les données ayant une structure de graphes sous-jacente peuvent être trouvées dans de nombreuses disciplines, décrivant des composés chimiques, des surfaces des modèles tridimensionnels, des interactions sociales ou des bases de connaissance, pour n'en nommer que quelques-unes. L'apprentissage profond (DL) a accompli des avancées significatives dans une variété de tâches d'apprentissage automatique au cours des dernières années, particulièrement lorsque les données sont structurées sur une grille

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Piano, Francesco. "Deep Reinforcement Learning con PyTorch." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25340/.

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Il Reinforcement Learning è un campo di ricerca del Machine Learning in cui la risoluzione di problemi da parte di un agente avviene scegliendo l’azione più idonea da eseguire attraverso un processo di apprendimento iterativo, in un ambiente dinamico che lo incentiva tramite ricompense. Il Deep Learning, anch’esso approccio del Machine Learning, sfruttando una rete neurale artificiale è in grado di applicare metodi di apprendimento per rappresentazione allo scopo di ottenere una struttura dei dati più idonea ad essere elaborata. Solo recentemente il Deep Reinforcement Learning, creato

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Broström, Axel, and Richard Kristiansson. "Exotic Derivatives and Deep Learning." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228476.

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This thesis investigates the use of Artificial Neural Networks (ANNs)for calculating present values, Value-at-Risk and Expected Shortfall ofoptions, both European call options and more complex rainbow options. Theperformance of the ANN is evaluated by comparing it to a second-order Taylorpolynomial using pre-calculated sensitivities to certain risk-factors. Amultilayer perceptron approach is chosen based on previous literature andapplied to both types of options. The data is generated from a financial risk-managementsoftware for both call options and rainbow options along with the relatedTaylo

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Watson, Cody. "Deep Learning In Software Engineering." W&M ScholarWorks, 2020. https://scholarworks.wm.edu/etd/1616444371.

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Software evolves and therefore requires an evolving field of Software Engineering. The evolution of software can be seen on an individual project level through the software life cycle, as well as on a collective level, as we study the trends and uses of software in the real world. As the needs and requirements of users change, so must software evolve to reflect those changes. This cycle is never ending and has led to continuous and rapid development of software projects. More importantly, it has put a great responsibility on software engineers, causing them to adopt practices and tools that al

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Kim, Alisa. "Deep Learning for Uncertainty Measurement." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22161.

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Diese Arbeit konzentriert sich auf die Lösung des Problems der Unsicherheitsmessung und ihrer Auswirkungen auf Geschäftsentscheidungen, wobei zwei Ziele verfolgt werden: Erstens die Entwicklung und Validierung robuster Modelle zur Quantifizierung der Unsicherheit, wobei insbesondere sowohl die etablierten statistischen Modelle als auch neu entwickelte maschinelle Lernwerkzeuge zum Einsatz kommen. Das zweite Ziel dreht sich um die industrielle Anwendung der vorgeschlagenen Modelle. Die Anwendung auf reale Fälle bei der Messung der Volatilität oder bei einer riskanten Entscheidung ist mit einem

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Rosar, Kós Lassance Carlos Eduardo. "Graphs for deep learning representations." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0204.

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Ces dernières années, les méthodes d'apprentissage profond ont atteint l'état de l'art dans une vaste gamme de tâches d'apprentissage automatique, y compris la classification d'images et la traduction automatique. Ces architectures sont assemblées pour résoudre des tâches d'apprentissage automatique de bout en bout. Afin d'atteindre des performances de haut niveau, ces architectures nécessitent souvent d'un très grand nombre de paramètres. Les conséquences indésirables sont multiples, et pour y remédier, il est souhaitable de pouvoir comprendre ce qui se passe à l'intérieur des architectures d

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Gunér, Gustaf. "Receipt Scanning Using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279697.

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Employees often make purchases on behalf of the companies that they are working for. These purchases must be reported manually, either by the employees themselves or by sending the receipts to the company‘s accountant. In both cases, parts of the receipts are transcribed manually. This process is time-consuming and poses a risk that the human factor causes transcription errors, which can lead to ambiguities in the company‘s financial statements. A fully-automatic receipt scanner, which from a photograph of a receipt can extract metadata (e.g. total price, VAT, and individual item names) would

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Elmarakeby, Haitham Abdulrahman. "Deep Learning for Biological Problems." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/86264.

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The last decade has witnessed a tremendous increase in the amount of available biological data. Different technologies for measuring the genome, epigenome, transcriptome, proteome, metabolome, and microbiome in different organisms are producing large amounts of high-dimensional data every day. High-dimensional data provides unprecedented challenges and opportunities to gain a better understanding of biological systems. Unlike other data types, biological data imposes more constraints on researchers. Biologists are not only interested in accurate predictive models that capture complex input-out

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Amar, Gilad. "Deep learning for supernovae detection." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27090.

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In future astronomical sky surveys it will be humanly impossible to classify the tens of thousands of candidate transients detected per night. This thesis explores the potential of using state-of-the-art machine learning algorithms to handle this burden more accurately and quickly than trained astronomers. To this end Deep Learning methods are applied to classify transients using real-world data from the Sloan Digital Sky Survey. Using cutting-edge training techniques several Convolutional Neural networks are trained and hyper-parameters tuned to outperform previous approaches and find that hu

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Stigenberg, Jakob. "Scheduling using Deep Reinforcement Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284506.

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As radio networks have continued to evolve in recent decades, so have theircomplexity and the difficulty in efficiently utilizing the available resources. Ina cellular network, the scheduler controls the allocation of time, frequencyand spatial resources to users in both uplink and downlink directions. Thescheduler is therefore a key component in terms of efficient usage of networkresources. Although the scope and characteristics of available resources forschedulers are well defined in network standards, e.g. Long-Term Evolutionor New Radio, its real implementation is not. Most previous work f

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Ramesh, Shreyas. "Deep Learning for Taxonomy Prediction." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/89752.

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The last decade has seen great advances in Next-Generation Sequencing technologies, and, as a result, there has been a rise in the number of genomes sequenced each year. In 2017, there were as many as 10,000 new organisms sequenced and added into the RefSeq Database. Taxonomy prediction is a science involving the hierarchical classification of DNA fragments up to the rank species. In this research, we introduce Predicting Linked Organisms, Plinko, for short. Plinko is a fully-functioning, state-of-the-art predictive system that accurately captures DNA - Taxonomy relationships where other state

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Xiao, Yao. "Vehicle Detection in Deep Learning." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91375.

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Computer vision techniques are becoming increasingly popular. For example, face recognition is used to help police find criminals, vehicle detection is used to prevent drivers from serious traffic accidents, and written word recognition is used to convert written words into printed words. With the rapid development of vehicle detection given the use of deep learning techniques, there are still concerns about the performance of state-of-the-art vehicle detection techniques. For example, state-of-the-art vehicle detectors are restricted by the large variation of scales. People working on vehicle

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Howard, Shaun Michael. "Deep Learning for Sensor Fusion." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1495751146601099.

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Abrishami, Hedayat. "Deep Learning Based Electrocardiogram Delineation." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273.

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Mansanet, Sandín Jorge. "Contributions to Deep Learning Models." Doctoral thesis, Universitat Politècnica de València, 2016. http://hdl.handle.net/10251/61296.

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[EN] Deep Learning is a new area of Machine Learning research which aims to create computational models that learn several representations of the data using deep architectures. These methods have become very popular over the last few years due to the remarkable results obtained in speech recognition, visual object recognition, object detection, natural language processing, etc. The goal of this thesis is to present some contributions to the Deep Learning framework, particularly focused on computer vision problems dealing with images. These contributions can be summarized in two novel methods

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Deselaers, Johannes. "Deep Learning Pupil Center Localization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287538.

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This project strives to achieve high performance object localization with Convolutional Neural Networks (CNNs) - in particular for pupil centers in the context of remote eye tracking systems. Three different network architectures suitable to the task are developed, evaluated and compared - one based on regression using fully connected layers, one Fully Convolutional Network and one Deconvolutional Network. The best performing model achieves a mean error of only 0.52 pixel distance and a median error of 0.42 pixel distance compared to the ground truth annotations. The 95th percentile lies at 1.

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MBITI, JOHN N. "Deep learning for portfolio optimization." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104567.

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In this thesis, an optimal investment problem is studied for an investor who can only invest in a financial market modelled by an Itô-Lévy process; with one risk free (bond) and one risky (stock) investment possibility. We present the dynamic programming method and the associated Hamilton-Jacobi-Bellman (HJB) equation to explicitly solve this problem. It is shown that with purification and simplification to the standard jump diffusion process, closed form solutions for the optimal investment strategy and for the value function are attainable. It is also shown that, an explicit solution can be

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Gerlach, Johanna, Alexander Riedel, Seyyid Uslu, Frank Engelmann, and Nico Brehm. "Montagegerechte Gestaltungsrichtlinien mittels Deep Learning." Thelem Universitätsverlag & Buchhandlung GmbH & Co. KG, 2021. https://tud.qucosa.de/id/qucosa%3A75857.

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Die Anwendung von Deep Learning in der manuellen Montage birgt großes Potenzial, Montagezeiten zu reduzieren und Montagefehler zu vermeiden. Indem der Montageablauf mithilfe einer Kamera erfasst und die aufgezeichneten Bilder durch einen Objekterkennungsalgorithmus analysiert werden, lassen sich Position, Lage und Art der montierten Bauteile bestimmen. Daraus lassen sich wiederum Informationen über Arbeitsschritte, Montagefehler oder den aktuellen Zustand des Produkts ableiten, sodass die Mitarbeiter bei der Montage durch entsprechende Anweisungen unterstützt werden können. Es stellt sich jedo

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Jaderberg, Maxwell. "Deep learning for text spotting." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:e893c11e-6b6b-4d11-bb25-846bcef9b13e.

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This thesis addresses the problem of text spotting - being able to automatically detect and recognise text in natural images. Developing text spotting systems, systems capable of reading and therefore better interpreting the visual world, is a challenging but wildly useful task to solve. We approach this problem by drawing on the successful developments in machine learning, in particular deep learning and neural networks, to present advancements using these data-driven methods. Deep learning based models, consisting of millions of trainable parameters, require a lot of data to train effectivel

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Ma, Sihan. "Image Matting via Deep Learning." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22426.

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Image matting aims to extract the accurate opacity of the foreground from the input RGB image, which is beneficial and essential for the subsequent applications, such as image editing, compositing, and film production. Unfortunately, this task is challenging because of its ill-posed nature. Specifically, with the corresponding foreground and background unknown, it is hard to predict the opacity of the foreground from the single RGB image. Recently, deep learning is introduced to image matting to deal with this problem. However, there are still some issues to be addressed. First, the choice of

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He, Haoyu. "Deep learning based human parsing." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/24262.

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Human parsing, or human body part semantic segmentation, has been an active research topic due to its wide potential applications. Although prior works have made significant progress by introducing large-scale datasets and deep learning to solve the problem, there are still two challenges remain unsolved. Firstly, to better exploit the existing parsing annotations, prior methods learn a knowledge-sharing mechanism to improve semantic structures in cross-dataset human parsing. However, the modeling for such mechanism remains inefficient for not considering classes' granularity difference in dif

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Zhao, Yang. "Person Retrieval with Deep Learning." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/411526.

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Person retrieval aims at matching person images across multiple non-overlapping camera views. It has facilitated a wide range of important applications in intelligent video analysis. The task of person retrieval remains challenging due to dramatic changes on visual appearance that are caused by large intra-class variations from human pose and camera viewpoint, misaligned person detection and occlusion. How to learn discriminative features under these challenging conditions becomes the core issue for the task of person retrieval. According to the input modality, person retrieval could be catego

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Ovidiu, Chelcea Vlad, and Björn Ståhl. "Deep Reinforcement Learning for Snake." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239362.

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The world has recently seen a large increase in both research and development and layman use of machine learning. Machine learning has a broad application domain, e.g, in marketing, production and finance. Although these applications have a predetermined set of rules or goals, this project deals with another aspect of machine learning which is general intelligence. During the course of the project a non-human player (known as agent) will learn how to play the game SNAKE without any outside influence or knowledge of the environment dynamics. After having the agent train for 66 hours and almost

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Figué, Valentin. "Depth prediction by deep learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240593.

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Knowing the depth information is of critical importance in scene understanding for several industrial projects such as self-driving cars for instance. Where depth inference from a single still image has taken a prominent place in recent studies with the outcome of deep learning methods, practical cases often offer useful additional information that should be considered early in the architecture of the design to benefit from them in order to improve quality and robustness of the estimates. Hence, this thesis proposes a deep fully convolutional network which allows to exploit the informations of

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Hussain, Jabbar. "Deep Learning Black Box Problem." Thesis, Uppsala universitet, Institutionen för informatik och media, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393479.

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Application of neural networks in deep learning is rapidly growing due to their ability to outperform other machine learning algorithms in different kinds of problems. But one big disadvantage of deep neural networks is its internal logic to achieve the desired output or result that is un-understandable and unexplainable. This behavior of the deep neural network is known as “black box”. This leads to the following questions: how prevalent is the black box problem in the research literature during a specific period of time? The black box problems are usually addressed by socalled rule extractio

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Li, Shuai Ph D. Massachusetts Institute of Technology. "Computational imaging through deep learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122070.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 143-154).<br>Computational imaging (CI) is a class of imaging systems that uses inverse algorithms to recover an unknown object from the physical measurement. Traditional inverse algorithms in CI obtain an estimate of the object by minimizing the T

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Dumas, Thierry. "Deep learning for image compression." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S029/document.

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Ces vingt dernières années, la quantité d’images et de vidéos transmises a augmenté significativement, ce qui est principalement lié à l’essor de Facebook et Netflix. Même si les capacités de transmission s’améliorent, ce nombre croissant d’images et de vidéos transmises exige des méthodes de compression plus efficaces. Cette thèse a pour but d’améliorer par l’apprentissage deux composants clés des standards modernes de compression d’image, à savoir la transformée et la prédiction intra. Plus précisément, des réseaux de neurones profonds sont employés car ils ont un grand pouvoir d’approximati

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Ouyang, Wei. "Deep Learning for Advanced Microscopy." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC174/document.

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Contexte: La microscopie joue un rôle important en biologie depuis plusieurs siècles, mais sa résolution a longtemps été limitée à environ 250 nm, de sorte que nombre de structures biologiques (virus, vésicules, pores nucléaires, synapses) ne pouvaient être résolues. Au cours de la dernière décennie, plusieurs méthodes de super-résolution ont été développées pour dépasser cette limite. Parmi ces techniques, les plus puissantes et les plus utilisées reposent sur la localisation de molécules uniques (microscopie à localisation de molécule unique, ou SMLM), comme PALM et STORM. En localisant préc

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Lomonaco, Vincenzo <1991&gt. "Continual Learning with Deep Architectures." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amsdottorato.unibo.it/9073/1/vincenzo_lomonaco_thesis.pdf.

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Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of Artificial Intelligence (AI) is building an artificial “continual learning” agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex knowledge and skills. However, despite early speculations and few pioneering works, very little research and effort has b

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