Relevant bibliographies by topics / Self-supervised learninig / Dissertations / Theses
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Dissertations / Theses on the topic 'Self-supervised learninig'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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1

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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Wang, Zhaoqing. "Self-supervised Visual Representation Learning." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29595.

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In general, large-scale annotated data are essential to training deep neural networks in order to achieve better performance in visual feature learning for various computer vision applications. Unfortunately, the amount of annotations is challenging to obtain, requiring a high cost of money and human resources. The dependence on large-scale annotated data has become a crucial bottleneck in developing an advanced intelligence perception system. Self-supervised visual representation learning, a subset of unsupervised learning, has gained popularity because of its ability to avoid the high cost
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Zaiem, Mohamed Salah. "Informed Speech Self-supervised Representation Learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT009.

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L'apprentissage des caractéristiques a été un des principaux moteurs des progrès de l'apprentissage automatique. L'apprentissage auto-supervisé est apparu dans ce contexte, permettant le traitement de données non étiquetées en vue d'une meilleure performance sur des tâches faiblement étiquetées. La première partie de mon travail de doctorat vise à motiver les choix dans les pipelines d'apprentissage auto-supervisé de la parole qui apprennent les représentations non supervisées. Dans cette thèse, je montre d'abord comment une fonction basée sur l'indépendance conditionnelle peut être utilisée p
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Ermolov, Aleksandr. "Self-supervised Representation Learning in Computer Vision and Reinforcement Learning." Doctoral thesis, Università degli studi di Trento, 2022. https://hdl.handle.net/11572/360781.

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This work is devoted to self-supervised representation learning (SSL). We consider both contrastive and non-contrastive methods and present a new loss function for SSL based on feature whitening. Our solution is conceptually simple and competitive with other methods. Self-supervised representations are beneficial for most areas of deep learning, and reinforcement learning is of particular interest because SSL can compensate for the sparsity of the training signal. We present two methods from this area. The first tackles the partial observability providing the agent with a history, represented
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Khan, Umair. "Self-supervised deep learning approaches to speaker recognition." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/671496.

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In speaker recognition, i-vectors have been the state-of-the-art unsupervised technique over the last few years, whereas x-vectors is becoming the state-of-the-art supervised technique, these days. Recent advances in Deep Learning (DL) approaches to speaker recognition have improved the performance but are constrained to the need of labels for the background data. In practice, labeled background data is not easily accessible, especially when large training data is required. In i-vector based speaker recognition, cosine and Probabilistic Linear Discriminant Analysis (PLDA) are the two basic sco
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6

Korecki, John Nicholas. "Semi-Supervised Self-Learning on Imbalanced Data Sets." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1686.

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Semi-supervised self-learning algorithms have been shown to improve classifier accuracy under a variety of conditions. In this thesis, semi-supervised self-learning using ensembles of random forests and fuzzy c-means clustering similarity was applied to three data sets to show where improvement is possible over random forests alone. Two of the data sets are emulations of large simulations in which the data may be distributed. Additionally, the ratio of majority to minority class examples in the training set was altered to examine the effect of training set bias on performance when applying the
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Zhang, Kun. "Supervised and Self-Supervised Learning for Video Object Segmentation in the Compressed Domain." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29361.

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Video object segmentation has attracted remarkable attention since it is more and more critical in real video understanding scenarios. Raw videos have very high redundancies. Therefore, using a heavy backbone network to extract features from all individual frames may be a waste of time. Also, the motion vectors and residuals in compressed videos provide motion information that can be utilized directly. Therefore, this thesis will discuss semi-supervised video object segmentation methods working directly on compressed videos. First, we discuss a supervised learning method for semi-supervised
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Govindarajan, Hariprasath. "Self-Supervised Representation Learning for Content Based Image Retrieval." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166223.

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Automotive technologies and fully autonomous driving have seen a tremendous growth in recent times and have benefitted from extensive deep learning research. State-of-the-art deep learning methods are largely supervised and require labelled data for training. However, the annotation process for image data is time-consuming and costly in terms of human efforts. It is of interest to find informative samples for labelling by Content Based Image Retrieval (CBIR). Generally, a CBIR method takes a query image as input and returns a set of images that are semantically similar to the query image. The
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Zangeneh, Kamali Fereidoon. "Self-supervised learning of camera egomotion using epipolar geometry." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286286.

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Visual odometry is one of the prevalent techniques for the positioning of autonomous agents equipped with cameras. Several recent works in this field have in various ways attempted to exploit the capabilities of deep neural networks to improve the performance of visual odometry solutions. One of such approaches is using an end-to-end learning-based solution to infer the egomotion of the camera from a sequence of input images. The state of the art end-to-end solutions employ a common self-supervised training strategy that minimises a notion of photometric error formed by the view synthesis of t
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Marsal, Rémi. "Motion analysis in videos with deep self-supervised learning." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS137.

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Ces travaux de thèse explorent les méthodes d'apprentissage auto-supervisé basées sur le mouvement dans les vidéos afin de réduire la dépendance à l'égard de coûteux ensembles de données annotées pour les tâches d'estimation du flux optique et de la profondeur monoculaire. En l'absence de vérité terrain, ces deux tâches sont principalement apprises par minimisation d'une erreur de reconstruction d'images en supposant l'hypothèse de constance de la luminosité vérifiée. Dans la pratique, en raison des variations de luminosité causées par des ombres mobiles ou des surfaces non lambertiennes, cett
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Coen, Michael Harlan. "Multimodal dynamics : self-supervised learning in perceptual and motor systems." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34022.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Includes bibliographical references (leaves 178-192).<br>This thesis presents a self-supervised framework for perceptual and motor learning based upon correlations in different sensory modalities. The brain and cognitive sciences have gathered an enormous body of neurological and phenomenological evidence in the past half century
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Sharma, Vivek [Verfasser], and R. [Akademischer Betreuer] Stiefelhagen. "Self-supervised Face Representation Learning / Vivek Sharma ; Betreuer: R. Stiefelhagen." Karlsruhe : KIT-Bibliothek, 2020. http://d-nb.info/1212512545/34.

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Baleia, José Rodrigo Ferreira. "Haptic robot-environment interaction for self-supervised learning in ground mobility." Master's thesis, Faculdade de Ciências e Tecnologia, 2014. http://hdl.handle.net/10362/12475.

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Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores<br>This dissertation presents a system for haptic interaction and self-supervised learning mechanisms to ascertain navigation affordances from depth cues. A simple pan-tilt telescopic arm and a structured light sensor, both fitted to the robot’s body frame, provide the required haptic and depth sensory feedback. The system aims at incrementally develop the ability to assess the cost of navigating in natural environments. For this purpose the robot learns a mapping between the appearance of objects, give
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Nyströmer, Carl. "Musical Instrument Activity Detection using Self-Supervised Learning and Domain Adaptation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280810.

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With the ever growing media and music catalogs, tools that search and navigate this data are important. For more complex search queries, meta-data is needed, but to manually label the vast amounts of new content is impossible. In this thesis, automatic labeling of musical instrument activities in song mixes is investigated, with a focus on ways to alleviate the lack of annotated data for instrument activity detection models. Two methods for alleviating the problem of small amounts of data are proposed and evaluated. Firstly, a self-supervised approach based on automatic labeling and mixing of
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Nett, Ryan. "Dataset and Evaluation of Self-Supervised Learning for Panoramic Depth Estimation." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2234.

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Depth detection is a very common computer vision problem. It shows up primarily in robotics, automation, or 3D visualization domains, as it is essential for converting images to point clouds. One of the poster child applications is self driving cars. Currently, the best methods for depth detection are either very expensive, like LIDAR, or require precise calibration, like stereo cameras. These costs have given rise to attempts to detect depth from a monocular camera (a single camera). While this is possible, it is harder than LIDAR or stereo methods since depth can't be measured from monocular
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GONZALEZ, JONAS PIERRE GUSTAVO. "Self-supervised solutions for developmental learning with the humanoid robot iCub." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1047609.

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For a long time, robots were assigned to repetitive tasks such as industrial chains, where social skills played a secondary role. However, as next-generation robots are designed to interact and collaborate with humans, it becomes ever more important to endow them with social competencies. Humans use several explicit and implicit cues, like gaze, facial expression, and gestures to communicate. To understand and acquire these skills, social interaction during infancy plays a crucial role. Babies already at birth show social skills and continue to learn and develop them during all childhood. As
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Denize, Julien. "Self-supervised representation learning and applications to image and video analysis." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMIR37.

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Dans cette thèse, nous développons des approches d'apprentissage auto-supervisé pour l'analyse d'images et de vidéos. L'apprentissage de représentation auto-supervisé permet de pré-entraîner les réseaux neuronaux à apprendre des concepts généraux sans annotations avant de les spécialiser plus rapidement à effectuer des tâches, et avec peu d'annotations. Nous présentons trois contributions à l'apprentissage auto-supervisé de représentations d'images et de vidéos. Premièrement, nous introduisons le paradigme théorique de l'apprentissage contrastif doux et sa mise en œuvre pratique appelée Estima
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Chéhab, L'Émir Omar. "Advances in Self-Supervised Learning : applications to neuroscience and sample-efficiency." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG079.

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L'apprentissage auto-supervisé a gagné en popularité en tant que méthode d'apprentissage à partir de données non annotées. Il s'agit essentiellement de créer puis de résoudre un problème de prédiction qui utilise les données; par exemple, de retrouver l'ordre de données qui ont été mélangées. Ces dernières années, cette approche a été utilisée avec succès pour entraîner des réseaux de neurones qui extraient des représentations utiles des données, le tout sans aucune annotation. Cependant, notre compréhension de ce qui est appris et de la qualité de cet apprentissage est limitée. Ce document éc
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Coursey, Kino High. "An Approach Towards Self-Supervised Classification Using Cyc." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5470/.

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Due to the long duration required to perform manual knowledge entry by human knowledge engineers it is desirable to find methods to automatically acquire knowledge about the world by accessing online information. In this work I examine using the Cyc ontology to guide the creation of Naïve Bayes classifiers to provide knowledge about items described in Wikipedia articles. Given an initial set of Wikipedia articles the system uses the ontology to create positive and negative training sets for the classifiers in each category. The order in which classifiers are generated and used to test articles
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Syrén, Grönfelt Natalie. "Pretraining a Neural Network for Hyperspectral Images Using Self-Supervised Contrastive Learning." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-179122.

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Hyperspectral imaging is an expanding topic within the field of computer vision, that uses images of high spectral granularity. Contrastive learning is a discrim- inative approach to self-supervised learning, a form of unsupervised learning where the network is trained using self-created pseudo-labels. This work com- bines these two research areas and investigates how a pretrained network based on contrastive learning can be used for hyperspectral images. The hyperspectral images used in this work are generated from simulated RGB images and spec- tra from a spectral library. The network is tra
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Lin, Lyu. "Transformer-based Model for Molecular Property Prediction with Self-Supervised Transfer Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284682.

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Molecular property prediction has a vast range of applications in the chemical industry. A powerful molecular property prediction model can promote experiments and production processes. The idea behind this degree program lies in the use of transfer learning to predict molecular properties. The project is divided into two parts. The first part is to build and pre-train the model. The model, which is constructed with pure attention-based Transformer Layer, is pre-trained through a Masked Edge Recovery task with large-scale unlabeled data. Then, the performance of this pre- trained model is test
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Graf, Saber. "Self-Supervised Learning for Improved Classification and Characterization of Sharp Wave Ripples." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0460.

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Les ondes rapides hippocampiques (ORH) sont des événements oscillatoires essentiels associés à la consolidation de la mémoire et au traitement cognitif. Cette recherche explore l'application de techniques d'apprentissage auto-supervisé (AAS) pour améliorer la classification des ORH, en se concentrant sur la distinction entre les événements avant et après l'apprentissage. Les méthodes traditionnelles échouent souvent à capturer les subtiles nuances des propriétés des ORH qui émergent à la suite de l'apprentissage. En intégrant l'AAS avec une architecture personnalisée de réseau de neurones conv
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Nisar, Zeeshan. "Self-supervised learning in the presence of limited labelled data for digital histopathology." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD016.

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Un défi majeur dans l'application de l'apprentissage profond à l'histopathologie réside dans la variation des colorations, à la fois inter et intra-coloration. Les modèles d'apprentissage profond entraînés sur une seule coloration (ou domaine) échouent souvent sur d'autres, même pour la même tâche (par exemple, la segmentation des glomérules rénaux). L'annotation de chaque coloration est coûteuse et chronophage, ce qui pousse les chercheurs à explorer des méthodes de transfert de coloration basées sur l'adaptation de domaine. Celles-ci visent à réaliser une segmentation multi-coloration en uti
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Doersch, Carl. "Supervision Beyond Manual Annotations for Learning Visual Representations." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/787.

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For both humans and machines, understanding the visual world requires relating new percepts with past experience. We argue that a good visual representation for an image should encode what makes it similar to other images, enabling the recall of associated experiences. Current machine implementations of visual representations can capture some aspects of similarity, but fall far short of human ability overall. Even if one explicitly labels objects in millions of images to tell the computer what should be considered similar—a very expensive procedure—the labels still do not capture everything th
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Brunetti, Enrico. "Sperimentazione di Deep Metric Loss per Self-Supervised Information Retrieval Systems su CORD19." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24295/.

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Dopo lo sviluppo dei primi casi di Covid-19 in Cina nell’autunno del 2019, ad inizio 2020 l’intero pianeta è precipitato in una pandemia globale che ha stravolto le nostre vite con conseguenze che non si vivevano dall’influenza spagnola. La grandissima quantità di paper scientifici in continua pubblicazione sul coronavirus e virus ad esso affini ha portato alla creazione di un unico dataset dinamico chiamato CORD19 e distribuito gratuitamente. Poter reperire informazioni utili in questa mole di dati ha ulteriormente acceso i riflettori sugli information retrieval systems, capaci di recuperare
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Feng, Zeyu. "Learning Deep Representations from Unlabelled Data for Visual Recognition." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26876.

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Self-supervised learning (SSL) aims at extracting from abundant unlabelled images transferable semantic features, which benefit various downstream visual tasks by reducing the sample complexity when human annotated labels are scarce. SSL is promising because it also boosts performance in diverse tasks when combined with the knowledge of existing techniques. Therefore, it is important and meaningful to study how SSL leads to better transferability and design novel SSL methods. To this end, this thesis proposes several methods to improve SSL and its function in downstream tasks. We begin by i
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Pannu, Husanbir Singh. "Semi-supervised and Self-evolving Learning Algorithms with Application to Anomaly Detection in Cloud Computing." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc177238/.

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Semi-supervised learning (SSL) is the most practical approach for classification among machine learning algorithms. It is similar to the humans way of learning and thus has great applications in text/image classification, bioinformatics, artificial intelligence, robotics etc. Labeled data is hard to obtain in real life experiments and may need human experts with experimental equipments to mark the labels, which can be slow and expensive. But unlabeled data is easily available in terms of web pages, data logs, images, audio, video les and DNA/RNA sequences. SSL uses large unlabeled and few labe
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Rosell, Mikael. "Semi-Supervised Learning for Object Detection." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-113560.

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Many automotive safety applications in modern cars make use of cameras and object detection to analyze the surrounding environment. Pedestrians, animals and other vehicles can be detected and safety actions can be taken before dangerous situations arise. To detect occurrences of the different objects, these systems are traditionally trained to learn a classification model using a set of images that carry labels corresponding to their content. To obtain high performance with a variety of object appearances, the required amount of data is very large. Acquiring unlabeled images is easy, while the
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Rossi, Alex. "Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic sear
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Walsh, Andrew Michael Graduate school of biomedical engineering UNSW. "Application of supervised and unsupervised learning to analysis of the arterial pressure pulse." Awarded by:University of New South Wales. Graduate school of biomedical engineering, 2006. http://handle.unsw.edu.au/1959.4/24841.

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This thesis presents an investigation of statistical analytical methods applied to the analysis of the shape of the arterial pressure waveform. The arterial pulse is analysed by a selection of both supervised and unsupervised methods of learning. Supervised learning methods are generally better known as regression. Unsupervised learning methods seek patterns in data without the specification of a target variable. The theoretical relationship between arterial pressure and wave shape is first investigated by study of a transmission line model of the arterial tree. A meta-database of pulse wavefo
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Ali, Shahnewaz. "Robotic vision for knee arthroscopy." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235890/1/Shahnewaz%2BAli%2BThesis%282%29.pdf.

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This research focuses on visualisation challenges associated with anatomical imaging of complex joints such as the human knee. Current imaging systems are inadequate to provide 3D perception and lack the level of situational awareness needed for performing highly complex minimally invasive surgeries like knee arthroscopy. As a result, unintended tissue damage is common occurrence and training new surgeons takes a very long time. To improve surgical precision and training, this study presents a series of novel methods and computational tools that provide 3D perception for safer surgery with add
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Geiler, Louis. "Deep learning for churn prediction." Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7333.

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Le problème de la prédiction de l’attrition est généralement réservé aux équipes de marketing. Cependant,grâce aux avancées technologiques, de plus en plus de données peuvent être collectés afin d’analyser le comportement des clients. C’est dans ce cadre que cette thèse s’inscrit, plus particulièrement par l’exploitation des méthodes d’apprentissages automatiques. Ainsi, nous avons commencés par étudier ce problème dans le cadre de l’apprentissage supervisé. Nous avons montré que la combinaison en ensemble de la régression logistique, des forêt aléatoire et de XGBoost offraient les meilleurs r
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Liu, Xialei. "Visual recognition in the wild: learning from rankings in small domains and continual learning in new domains." Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/670154.

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Les xarxes neuronals convolucionals profundes (CNNs) han assolit resultats molt positius en diverses aplicacions de reconeixement visual, tals com classificació, detecció o segmentació d’imatges. En aquesta tesis, abordem dues limitacions de les CNNs. La primera, entrenar CNNs profundes requereix grans quantitats de dades etiquetades, les quals són molt costoses i àrdues d’aconseguir. La segona és que entrenar CNNs en sistemes d’aprenentatge continuu és un problema obert per a la recerca. L’oblit catastròfic en xarxes és molt comú quan s’adapta un model entrenat a nous entorns o noves t
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Buttar, Sarpreet Singh. "Applying Machine Learning to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-77201.

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Self-adaptive systems are capable of autonomously adjusting their behavior at runtime to accomplish particular adaptation goals. The most common way to realize self-adaption is using a feedback loop(s) which contains four actions: collect runtime data from the system and its environment, analyze the collected data, decide if an adaptation plan is required, and act according to the adaptation plan for achieving the adaptation goals. Existing approaches achieve the adaptation goals by using formal methods, and exhaustively verify all the available adaptation options, i.e., adaptation space. Howe
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BORDONE, MOLINI ANDREA. "Deep learning for inverse problems in remote sensing: super-resolution and SAR despeckling." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2903492.

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Ciocarlan, Alina. "Small target detection using deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG102.

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La détection de petits objets dans les images infrarouges (IR) est une tâche complexe mais cruciale en défense, surtout lorsqu'il s'agit de distinguer ces cibles d'un fond texturé. Les méthodes de détection d'objets classiques peinent à trouver un équilibre entre un taux de détection élevé et un faible taux de fausses alarmes. Bien que certaines approches aient amélioré les réponses des cartes de caractéristiques pour les petits objets, elles restent tout de même sensibles aux fausses alarmes induites par les éléments du fond. Pour résoudre ce problème, la première partie de cette thèse introd
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Braga, Ígor Assis. "Aprendizado semissupervisionado multidescrição em classificação de textos." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-02062010-160019/.

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Algoritmos de aprendizado semissupervisionado aprendem a partir de uma combinação de dados rotulados e não rotulados. Assim, eles podem ser aplicados em domínios em que poucos exemplos rotulados e uma vasta quantidade de exemplos não rotulados estão disponíveis. Além disso, os algoritmos semissupervisionados podem atingir um desempenho superior aos algoritmos supervisionados treinados nos mesmos poucos exemplos rotulados. Uma poderosa abordagem ao aprendizado semissupervisionado, denominada aprendizado multidescrição, pode ser usada sempre que os exemplos de treinamento são descritos por dois
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Victorino, Cardoso Gabriel. "Generative models for ECG data : theory and application." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX022.

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Cette thèse apporte des contributions au vaste domaine des modèles génératifs, avec un intérêt particulier pour l'application de tels modèles aux données d'électrocardiogramme (ECG) dans le cadre de l'inférence et de la quantification de l'incertitude.Dans une première partie, nous développons deux méthodes novatrices pour réduire le biais dans les méthodes d'échantillonnage d'importance et de Monte Carlo séquentiel (SMC), qui sont deux outils importants de l'inférence bayésienne. Les algorithmes résultants peuvent être considérés tous deux comme des "enveloppes" autour d'algorithmes existants
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Cavallucci, Martina. "Speech Recognition per l'italiano: Sviluppo e Sperimentazione di Soluzioni Neurali con Language Model." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Le e-mail e i servizi di messaggistica hanno cambiato significativamente la comunicazione umana, ma la parola è ancora il metodo più importante di comunicazione tra esseri umani. Pertanto, il riconoscimento vocale automatico (ASR) è di particolare rilevanza perché fornisce una trascrizione della lingua parlata che può essere valutata da sistemi automatizzati. Con altoparlanti intelligenti come Google Home, Alexa o Siri, l' ASR è già un parte integrante di molte famiglie ed è usato per suonare musica, rispondere alle domande o controllare altri dispositivi intelligenti come un sistema di do
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Araújo, Hiury Nogueira de. "Utilizando aprendizado emissupervisionado multidescrição em problemas de classificação hierárquica multirrótulo." Universidade Federal Rural do Semi-Árido, 2017. http://bdtd.ufersa.edu.br:80/tede/handle/tede/839.

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Submitted by Lara Oliveira (lara@ufersa.edu.br) on 2018-03-14T20:25:58Z No. of bitstreams: 1 HiuryNA_DISSERT.pdf: 3188162 bytes, checksum: d40d42a78787557868ebc6d3cd5af945 (MD5)<br>Approved for entry into archive by Vanessa Christiane (referencia@ufersa.edu.br) on 2018-06-18T16:58:58Z (GMT) No. of bitstreams: 1 HiuryNA_DISSERT.pdf: 3188162 bytes, checksum: d40d42a78787557868ebc6d3cd5af945 (MD5)<br>Approved for entry into archive by Vanessa Christiane (referencia@ufersa.edu.br) on 2018-06-18T16:59:18Z (GMT) No. of bitstreams: 1 HiuryNA_DISSERT.pdf: 3188162 bytes, checksum: d40d42a78787557868ebc
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Coletta, Luiz Fernando Sommaggio. "Abordagens para combinar classificadores e agrupadores em problemas de classificação." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-24032016-102229/.

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Modelos para aprendizado não supervisionado podem fornecer restrições complementares úteis para melhorar a capacidade de generalização de classificadores. Baseando-se nessa premissa, um algoritmo existente, denominado de C3E (Consensus between Classification and Clustering Ensembles), recebe como entradas estimativas de distribuições de probabilidades de classes para objetos de um conjunto alvo, bem como uma matriz de similaridades entre esses objetos. Tal matriz é tipicamente construída por agregadores de agrupadores de dados, enquanto que as distribuições de probabilidades de classes são obt
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FAMOURI, SINA. "Machine learning methods for the analysis and interpretation of images and other multi-dimensional data." Doctoral thesis, Politecnico di Torino, 2022. https://hdl.handle.net/11583/2972835.

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Venkataramanan, Shashanka. "Metric learning for instance and category-level visual representation." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS022.

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Le principal objectif de la vision par ordinateur est de permettre aux machines d'extraire des informations significatives à partir de données visuelles, telles que des images et des vidéos, et de tirer parti de ces informations pour effectuer une large gamme de tâches. À cette fin, de nombreuses recherches se sont concentrées sur le développement de modèles d'apprentissage profond capables de coder des représentations visuelles complètes et robustes. Une stratégie importante dans ce contexte consiste à préentraîner des modèles sur des ensembles de données à grande échelle, tels qu'ImageNet, p
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Jezequel, Loïc. "Vers une détection d'anomalie unifiée avec une application à la détection de fraude." Electronic Thesis or Diss., CY Cergy Paris Université, 2023. http://www.theses.fr/2023CYUN1190.

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La détection d'observation s'écartant d'un cas de référence est cruciale dans de nombreuses applications. Cette problématique est présente dans la détection de fraudes, l'imagerie médicale, voire même la surveillance vidéo avec des données allant d'image aux sons. La détection d'anomalie profonde a été introduite dans cette optique, en modélisant la classe normale et en considérant tout ce qui est significativement différent comme étant anormal. Dans la mesure où la classe anormale n'est pas bien définie, une classification binaire classique manquerait de robustesse et de fiabilité sur des don
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Banville, Hubert. "Enabling real-world EEG applications with deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG005.

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Au cours des dernières décennies, les avancées révolutionnaires en neuroimagerie ont permis de considérablement améliorer notre compréhension du cerveau. Aujourd'hui, avec la disponibilité croissante des dispositifs personnels de neuroimagerie portables, tels que l'EEG mobile " à bas prix ", une nouvelle ère s’annonce où cette technologie n'est plus limitée aux laboratoires de recherche ou aux contextes cliniques. Les applications de l’EEG dans le " monde réel " présentent cependant leur lot de défis, de la rareté des données étiquetées à la qualité imprévisible des signaux et leur résolution
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de, Carvalho Emanuel. "Relatório no âmbito da unidade curricular prática de ensino supervisionada, realizada na Escola Secundária/3 Rainha Santa Isabel de Estremoz." Master's thesis, Universidade de Évora, 2016. http://hdl.handle.net/10174/20092.

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O presente relatório foi produzido no âmbito da unidade curricular Prática de Ensino Supervisionada, que faz parte do Mestrado em Ensino do Português no 3º Ciclo do Ensino Básico e Ensino Secundário e de Espanhol nos Ensinos Básico e Secundário, sob a orientação da Professora Doutora Ângela Maria Franco Martins Coelho de Paiva Balça. Identifica-se, na sua essência basilar, como um trabalho reflexivo-descritivo sobre a prática aplicada e efetuada no ano letivo 2015/2016, no lecionamento das disciplinas de Português em duas turmas de 10º ano, e de Espanhol – Língua Estrangeira I numa de 7º ano,
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Gotab, Pierre. "Classification automatique pour la compréhension de la parole : vers des systèmes semi-supervisés et auto-évolutifs." Phd thesis, Université d'Avignon, 2012. http://tel.archives-ouvertes.fr/tel-00858980.

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La compréhension automatique de la parole est au confluent des deux grands domaines que sont la reconnaissance automatique de la parole et l'apprentissage automatique. Un des problèmes majeurs dans ce domaine est l'obtention d'un corpus de données conséquent afin d'obtenir des modèles statistiques performants. Les corpus de parole pour entraîner des modèles de compréhension nécessitent une intervention humaine importante, notamment dans les tâches de transcription et d'annotation sémantique. Leur coût de production est élevé et c'est la raison pour laquelle ils sont disponibles en quantité lim
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Nazifi, Nahid. "Transformer-Based Visual Odometry and DepthEstimation for Wireless Capsule Endoscopy." Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2025. http://www.theses.fr/2025ISAB0002.

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L'estimation précise de la pose et de la profondeur pour l'endoscopie par capsule (Wireless Capsule Endoscopy, WCE) demeure un défi majeur en raison de la nature non structurée et pauvre en textures du tractus gastro-intestinal (GI). Cette thèse explore l'utilisation d'architectures basées sur les transformateurs pour l'estimation auto-supervisée de la profondeur et de la pose monoculaires en WCE. Contrairement aux méthodes traditionnelles d'odométrie visuelle, qui reposent sur des techniques basées sur des points d'intérêt, les approches proposées exploitent le Pyramid Vision Transformer (PVT
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Duarte, Maisa Cristina. "Aprendizado semissupervisionado através de técnicas de acoplamento." Universidade Federal de São Carlos, 2011. https://repositorio.ufscar.br/handle/ufscar/474.

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Made available in DSpace on 2016-06-02T19:05:51Z (GMT). No. of bitstreams: 1 3777.pdf: 3225691 bytes, checksum: 38e3ba8f3c842f4e05d42710339e897a (MD5) Previous issue date: 2011-02-17<br>Machine Learning (ML) can be seen as research area within the Artificial Intelligence (AI) that aims to develop computer programs that can evolve with new experiences. The main ML purpose is the search for methods and techniques that enable the computer system improve its performance autonomously using information learned through its use. This feature can be considered the fundamental mechanisms of the proc
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Zheng, Léon. "Frugalité en données et efficacité computationnelle dans l'apprentissage profond." Electronic Thesis or Diss., Lyon, École normale supérieure, 2024. http://www.theses.fr/2024ENSL0009.

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Cette thèse s’intéresse à deux enjeux de frugalité et d’efficacité dans l’apprentissage profond moderne : frugalité en données et efficacité en ressources de calcul. Premièrement, nous étudions l’apprentissage auto-supervisé, une approche prometteuse en vision par ordinateur qui ne nécessite pas d’annotations des données pour l'apprentissage de représentations. En particulier, nous proposons d’unifier plusieurs fonctions objectives auto-supervisées dans un cadre de noyaux invariants par rotation, ce qui ouvre des perspectives en termes de réduction de coût de calcul de ces fonctions objectives
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