Relevant bibliographies by topics / Transfer of Learning / Dissertations / Theses
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Dissertations / Theses on the topic 'Transfer of Learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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1

Shell, Jethro. "Fuzzy transfer learning." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/8842.

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The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs). The sparsity of the data can be a result of the physical nature of the implementation, such as sensors placed into disaster recovery scenarios, or where the focus of the data acquisition is on very defined user groups, in the case of disabled individuals. Standard machine learnin
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2

Lu, Ying. "Transfer Learning for Image Classification." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEC045/document.

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Lors de l’apprentissage d’un modèle de classification pour un nouveau domaine cible avec seulement une petite quantité d’échantillons de formation, l’application des algorithmes d’apprentissage automatiques conduit généralement à des classifieurs surdimensionnés avec de mauvaises compétences de généralisation. D’autre part, recueillir un nombre suffisant d’échantillons de formation étiquetés manuellement peut s’avérer très coûteux. Les méthodes de transfert d’apprentissage visent à résoudre ce type de problèmes en transférant des connaissances provenant d’un domaine source associé qui contient
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Alexander, John W. "Transfer in reinforcement learning." Thesis, University of Aberdeen, 2015. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908.

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The problem of developing skill repertoires autonomously in robotics and artificial intelligence is becoming ever more pressing. Currently, the issues of how to apply prior knowledge to new situations and which knowledge to apply have not been sufficiently studied. We present a transfer setting where a reinforcement learning agent faces multiple problem solving tasks drawn from an unknown generative process, where each task has similar dynamics. The task dynamics are changed by varying in the transition function between states. The tasks are presented sequentially with the latest task presente
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4

Kiehl, Janet K. "Learning to Change: Organizational Learning and Knowledge Transfer." online version, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1080608710.

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5

Johnson, C. Dustin. "Set-Switching and Learning Transfer." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/psych_hontheses/7.

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In this experiment I investigated the relationship between set-switching and transfer learning, both of which presumably invoke executive functioning (EF), which may in turn be correlated with intelligence. Set-switching was measured by a computerized version of the Wisconsin Card Sort Task. Another computer task was written to measure learning-transfer ability. The data indicate little correlation between the ability to transfer learning and the capacity for set-switching. That is, these abilities may draw from independent cognitive mechanisms. The major difference may be requirement to
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6

Skolidis, Grigorios. "Transfer learning with Gaussian processes." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6271.

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Transfer Learning is an emerging framework for learning from data that aims at intelligently transferring information between tasks. This is achieved by developing algorithms that can perform multiple tasks simultaneously, as well as translating previously acquired knowledge to novel learning problems. In this thesis, we investigate the application of Gaussian Processes to various forms of transfer learning with a focus on classification problems. This process initiates with a thorough introduction to the framework of Transfer learning, providing a clear taxonomy of the areas of research. Foll
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7

Chen, Xiaoyi. "Transfer Learning with Kernel Methods." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0005.

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Le transfert d‘apprentissage regroupe les méthodes permettant de transférer l’apprentissage réalisé sur des données (appelées Source) à des données nouvelles, différentes, mais liées aux données Source. Ces travaux sont une contribution au transfert d’apprentissage homogène (les domaines de représentation des Source et Cible sont identiques) et transductif (la tâche à effectuer sur les données Cible est identique à celle sur les données Source), lorsque nous ne disposons pas d’étiquettes des données Cible. Dans ces travaux, nous relâchons la contrainte d’égalité des lois des étiquettes conditi
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8

Al, Chalati Abdul Aziz, and Syed Asad Naveed. "Transfer Learning for Machine Diagnostics." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43185.

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Fault detection and diagnostics are crucial tasks in condition-based maintenance. Industries nowadays are in need of fault identification in their machines as early as possible to save money and take precautionary measures in case of fault occurrence. Also, it is beneficial for the smooth interference in the manufacturing process in which it avoids sudden malfunctioning. Having sufficient training data for industrial machines is also a major challenge which is a prerequisite for deep neural networks to train an accurate prediction model. Transfer learning in such cases is beneficial as it can
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9

Arnekvist, Isac. "Transfer Learning using low-dimensional Representations in Reinforcement Learning." Licentiate thesis, KTH, Robotik, perception och lärande, RPL, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279120.

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Successful learning of behaviors in Reinforcement Learning (RL) are often learned tabula rasa, requiring many observations and interactions in the environment. Performing this outside of a simulator, in the real world, often becomes infeasible due to the large amount of interactions needed. This has motivated the use of Transfer Learning for Reinforcement Learning, where learning is accelerated by using experiences from previous learning in related tasks. In this thesis, I explore how we can transfer from a simple single-object pushing policy, to a wide array of non-prehensile rearrangement pr
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10

Mare, Angelique. "Motivators of learning and learning transfer in the workplace." Diss., University of Pretoria, 2015. http://hdl.handle.net/2263/52441.

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Motivating employees to learn and transfer their learning to their jobs is an important activity to ensure that employees - and the organisation - continuously adapt, evolve and survive in this highly turbulent environment. The literature shows that both intrinsic and extrinsic motivators influence learning and learning transfer, and the extent of influence could be different for different people. This research sets out to explore and identify the intrinsic and extrinsic motivational factors that drive learning and learning transfer. A qualitative study in the form of focus groups was conduct
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11

Redko, Ievgen. "Nonnegative matrix factorization for transfer learning." Thesis, Sorbonne Paris Cité, 2015. http://www.theses.fr/2015USPCD059.

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L’apprentissage par transfert consiste `a utiliser un jeu de taches pour influencerl’apprentissage et améliorer les performances sur une autre tache.Cependant, ce paradigme d’apprentissage peut en réalité gêner les performancessi les taches (sources et cibles) sont trop dissemblables. Un défipour l’apprentissage par transfert est donc de développer des approchesqui détectent et évitent le transfert négatif des connaissances utilisant tr`espeu d’informations sur la tache cible. Un cas particulier de ce type d’apprentissageest l’adaptation de domaine. C’est une situation o`u les tachessources et
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12

Marquer, Esteban. "Reasoning over Data : Analogy-based and Transfer Learning to improve Machine Learning." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0056.

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Ces dernières années ont été marquées par un regain d'intérêt pour le potentiel de l'inférence analogique et de la détection des analogies, avec des applications fructueuses dans l'apprentissage automatique pour la découverte et la génération d'images, de textes et de données structurées telles que les graphes de connaissances, ou encore la détection des relations entre et au sein d'images, de textes, ou de données structurées. Si certains de ces travaux reposent sur une compréhension intuitive de l'analogie, des efforts considérables ont été déployés depuis l'Antiquité pour définir les analog
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13

Frenger, Tobias, and Johan Häggmark. "Transfer learning between domains : Evaluating the usefulness of transfer learning between object classification and audio classification." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18669.

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Convolutional neural networks have been successfully applied to both object classification and audio classification. The aim of this thesis is to evaluate the degree of how well transfer learning of convolutional neural networks, trained in the object classification domain on large datasets (such as CIFAR-10, and ImageNet), can be applied to the audio classification domain when only a small dataset is available. In this work, four different convolutional neural networks are tested with three configurations of transfer learning against a configuration without transfer learning. This allows for
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14

Mallia, Gorg. "Transfer of learning from literature lessons." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274972.

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15

Quattoni, Ariadna J. "Transfer learning algorithms for image classification." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53294.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 124-128).<br>An ideal image classifier should be able to exploit complex high dimensional feature representations even when only a few labeled examples are available for training. To achieve this goal we develop transfer learning algorithms that: 1) Leverage unlabeled data annotated with meta-data and 2) Exploit labeled data from related categories. In the first part of this thesis we show how to
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16

Aytar, Yusuf. "Transfer learning for object category detection." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:c9e18ff9-df43-4f67-b8ac-28c3fdfa584b.

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Object category detection, the task of determining if one or more instances of a category are present in an image with their corresponding locations, is one of the fundamental problems of computer vision. The task is very challenging because of the large variations in imaged object appearance, particularly due to the changes in viewpoint, illumination and intra-class variance. Although successful solutions exist for learning object category detectors, they require massive amounts of training data. Transfer learning builds upon previously acquired knowledge and thus reduces training requirement
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17

Farajidavar, Nazli. "Transductive transfer learning for computer vision." Thesis, University of Surrey, 2015. http://epubs.surrey.ac.uk/807998/.

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Artificial intelligent and machine learning technologies have already achieved significant success in classification, regression and clustering. However, many machine learning methods work well only under a common assumption that training and test data are drawn from the same feature space and the same distribution. A real world applications is in sports footage, where an intelligent system has been designed and trained to detect score-changing events in a Tennis single match and we are interested to transfer this learning to either Tennis doubles game or even a more challenging domain such as
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18

Jamil, Ahsan Adnan, and Daniel Landberg. "Detecting COVID-19 Using Transfer Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280352.

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COVID-19 is currently an ongoing pandemic and the large demand for testing of the disease has led to insufficient resources in hospitals. In order to increase the efficiency of COVID- 19 detection, computer vision based systems can be used. However, a large set of training data is required for creating an accurate and reliable model, which is currently not feasible to be acquired considering the novelty of the disease. Other models are currently being used within the healthcare sector for classifying various diseases, one such model is for identifying pneumonia cases by using radiographs and i
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19

Mendoza-Schrock, Olga L. "Diffusion Maps and Transfer Subspace Learning." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1503964976467066.

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20

Kumar, Sharad. "Localizing Little Landmarks with Transfer Learning." PDXScholar, 2019. https://pdxscholar.library.pdx.edu/open_access_etds/4827.

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Locating a small object in an image -- like a mouse on a computer desk or the door handle of a car -- is an important computer vision problem to solve because in many real life situations a small object may be the first thing that gets operated upon in the image scene. While a significant amount of artificial intelligence and machine learning research has focused on localizing prominent objects in an image, the area of small object detection has remained less explored. In my research I explore the possibility of using context information to localize small objects in an image. Using a Convoluti
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21

Daniel, Filippo <1995&gt. "Transfer learning with generative adversarial networks." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/16989.

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Generative Adversarial Networks (GANs) emerged in recent years as the undiscussed SotA for image synthesis. This model leverages the recent successes of convolutional networks in the field of computer vision to learn the probability distribution of image datasets. Following the first proposal of GANs, many developments and usages of the models have been proposed. This thesis aims to review the evolution of the model and use one of the most recent variations to generate realistic portrait images with a targeted set of features. The usage of this model will be applied in a transfer learning appr
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22

Bose, Tulika. "Transfer learning for abusive language detection." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0019.

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La prolifération des médias sociaux, malgré ses nombreux avantages, a entraîné une augmentation des propos injurieux. Ces propos, qui sont généralement blessants, toxiques ou empreints de préjugés à l'encontre d'individus ou de groupes, doivent être détectés et modérés rapidement par les plateformes en ligne. Les modèles d'apprentissage profond pour la détection de propos abusifs ont montré des niveaux de performance élevé quand ils sont évalués sur des données similaires à celles qui ont servi à entraîner les modèles, mais sont nettement moins performants s'ils sont évalués sur des données do
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23

Pégeot, Tom. "Efficient Transfer Learning Towards Constrained Environments." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG101.

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Cette thèse explore des techniques d'apprentissage par transfert efficientes pour des environnements contraints, où la réduction du nombre de paramètres ajustables devient centrale. Dans un premier temps, nous examinons l'impact de la quantité de données de pré-entraînement ainsi que du nombre de classes associées. Lors du transfert, nous étudions également la méthode de transfert employée ainsi que le nombre d'exemples par classe dans la tâche cible. Nos résultats montrent que, durant le pré-entraînement, les performances saturent après une certaine quantité de données, et qu'une fois ce seui
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Choi, Jin-Woo. "Action Recognition with Knowledge Transfer." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/101780.

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Recent progress on deep neural networks has shown remarkable action recognition performance from videos. The remarkable performance is often achieved by transfer learning: training a model on a large-scale labeled dataset (source) and then fine-tuning the model on the small-scale labeled datasets (targets). However, existing action recognition models do not always generalize well on new tasks or datasets because of the following two reasons. i) Current action recognition datasets have a spurious correlation between action types and background scene types. The models trained on these datasets a
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Lieu, Jenny. "Influences of policy learning, transfer, and post transfer learning in the development of China's wind power policies." Thesis, University of Sussex, 2013. http://sro.sussex.ac.uk/id/eprint/46453/.

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China's renewable energy (RE) sector is developing rapidly, driven by growing energy needs, increased awareness of climate change, and heightened concerns for environmental degradation caused by the country's industrialisation process over the past decades. The Chinese government has been dedicated to the development of its RE industry and has engaged extensively in drawing lessons from abroad and applying these lessons to its own experiences in the post transfer learning process to develop policies that have contributed to the development of the largest wind power sector in the world. This th
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Pettersson, Harald. "Sentiment analysis and transfer learning using recurrent neural networks : an investigation of the power of transfer learning." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-161348.

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In the field of data mining, transfer learning is the method of transferring knowledge from one domain into another. Using reviews from prisjakt.se, a Swedish price comparison site, and hotels.com this work investigate how the similarities between domains affect the results of transfer learning when using recurrent neural networks. We test several different domains with different characteristics, e.g. size and lexical similarity. In this work only relatively similar domains were used, the same target function was sought and all reviews were in Swedish. Regardless, the results are conclusive; t
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Andersen, Linda, and Philip Andersson. "Deep Learning Approach for Diabetic Retinopathy Grading with Transfer Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279981.

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Diabetic retinopathy (DR) is a complication of diabetes and is a disease that affects the eyes. It is one of the leading causes of blindness in the Western world. As the number of people with diabetes grows globally, so does the number of people affected by diabetic retinopathy. This demand requires that better and more effective resources are developed in order to discover the disease in an early stage which is key to preventing that the disease progresses into more serious stages which ultimately could lead to blindness, and streamline further treatment of the disease. However, traditional m
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Shermin, Tasfia. "Enhancing deep transfer learning for image classification." Thesis, Federation University Australia, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/179551.

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Though deep learning models require a large amount of labelled training data for yielding high performance, they are applied to accomplish many computer vision tasks such as image classification. Current models also do not perform well across different domain settings such as illumination, camera angle and real-to-synthetic. Thus the models are more likely to misclassify unknown classes as known classes. These issues challenge the supervised learning paradigm of the models and encourage the study of transfer learning approaches. Transfer learning allows us to utilise the knowledge acquired fro
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Toll, Debora K. "The transfer of learning: Employees' lived experiences." Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/29178.

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The employees' ability to continuously and collectively learn, and to apply their learning are critical to their own and their organization's performance. This study, therefore, sought to understand employees' perceptions of and experiences with the application of or, transfer of their learning. It also sought to understand the interplay between the three primary transfer sources. The overarching research question that guided this study was what were employees' lived experiences with transfer? The subquestions were how do employees transfer their learning, when did transfer enter their learnin
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Masko, David. "Calibration in Eye Tracking Using Transfer Learning." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210815.

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This thesis empirically studies transfer learning as a calibration framework for Convolutional Neural Network (CNN) based appearance-based gaze estimation models. A dataset of approximately 1,900,000 eyestripe images distributed over 1682 subjects is used to train and evaluate several gaze estimation models. Each model is initially trained on the training data resulting in generic gaze models. The models are subsequently calibrated for each test subject, using the subject's calibration data, by applying transfer learning through network fine-tuning on the final layers of the network. Transfer
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Maehle, Valerie A. "Conceptual models in the transfer of learning." Thesis, University of Aberdeen, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261454.

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In order to attain clinical competence student physiotherapists apply knowledge from a range of cognitive domains in the assessment and treatment of patients with a variety of conditions. Current research indicates that the ability to transfer knowledge to a wide variety of conditions requires a cognitive structure in which concepts are embedded in a rich network of interconnections (Faletti, 1990, Spiro, 1987). A concept mapping technique was selected as means of eliciting a representation of the knowledge the student possessed and would access in order to underpin the assessment and treatmen
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Kodirov, Elyor. "Cross-class transfer learning for visual data." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31852.

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Automatic analysis of visual data is a key objective of computer vision research; and performing visual recognition of objects from images is one of the most important steps towards understanding and gaining insights into the visual data. Most existing approaches in the literature for the visual recognition are based on a supervised learning paradigm. Unfortunately, they require a large amount of labelled training data which severely limits their scalability. On the other hand, recognition is instantaneous and effortless for humans. They can recognise a new object without seeing any visual sam
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Boyer, Sebastien (Sebastien Arcario). "Transfer learning for predictive models in MOOCs." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104832.

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Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2016.<br>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 85-87).<br>Predictive models are crucial in enabling the personalization of student experiences in Massive Open Online Courses. For successful real-time interventions, these models must be transferable - that is,
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Scahill, Victoria Louise. "Perceptual learning and transfer along a continuum." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620585.

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Grönlund, Lucas. "Transfer learning in Swedish - Twitter sentiment classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252536.

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Language models can be applied to a diverse set of tasks with great results, but training a language model can unfortunately be a costly task, both in time and money. By transferring knowledge from one domain to another, the costly training only has to be performed once, thus opening the door for more applications. Most current research is carried out with English as the language of choice, thus limiting the amount of available already trained language models in other languages. This thesis explores how the amount of data available for training a language model effects the performance on a Twi
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Pang, Jinyong. "Human Activity Recognition Based on Transfer Learning." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7558.

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Human activity recognition (HAR) based on time series data is the problem of classifying various patterns. Its widely applications in health care owns huge commercial benefit. With the increasing spread of smart devices, people have strong desires of customizing services or product adaptive to their features. Deep learning models could handle HAR tasks with a satisfied result. However, training a deep learning model has to consume lots of time and computation resource. Consequently, developing a HAR system effectively becomes a challenging task. In
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Cifka, Ondrej. "Deep learning methods for music style transfer." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT029.

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Récemment, les méthodes d'apprentissage profond ont permis d'effectuer des transformations du matériel musical basées sur les données (data-driven). L'objet de cette thèse est le transfert de style musical, dont le but est de transférer de manière automatique le style d'un morceau à un autre. Dans la première partie de ce travail, nous nous concentrons sur les méthodes supervisées pour le transfert de style des accompagnements dans une représentation symbolique, visant à transformer un morceau donné en lui générant un nouvel accompagnement. La méthode proposée est basée sur l'apprentissage sup
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Broqvist, Widham Emil. "Scaling up Maximum Entropy Deep Inverse Reinforcement Learning with Transfer Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281796.

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In this thesis an issue with common inverse reinforcement learning algorithms is identified, which causes them to be computationally heavy. A solution is proposed which attempts to address this issue and which can be built upon in the future. The complexity of inverse reinforcement algorithms is increased because at each iteration something called a reinforcement learning step is performed to evaluate the result of the previous iteration and guide future learning. This step is slow to perform for problems with large state spaces and where many iterations are required. It has been observed that
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Juozapaitis, Jeffrey James. "Exploring Supervised Many Layered Learning as a Precursor to Transfer Learning." Thesis, The University of Arizona, 2012. http://hdl.handle.net/10150/271607.

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In this paper, we learn a simple conceptual card game as learned by David Stracuzzi's Cumulus algorithm. We then posit a (sadly unimplemented) scheme to transfer the neural net created by it to a similar game with small modifications, hopefully cutting down the learning time. We then analyze the flaws with the transfer scheme and posit other schemes that may produce better results.
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Groneman, Kathryn Jane. "The Trouble with Transfer." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/2164.

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It is hoped that the scientific reasoning skills taught in our biology courses will carry over to be applied in novel settings: to new concepts, future courses, other disciplines, and non-academic pursuits. This is the educational concept of transfer. Efforts over many years in the Cell Biology course at BYU to design effective assessment questions that measure competence in both deep understanding of conceptual principles and the ability to draw valid conclusions from experimental data have had at least one disquieting result. The transfer performance of many otherwise capable students is not
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Shalabi, Kholood Matouq. "Motor learning and inter-manual transfer of motor learning after a stroke." Thesis, University of Newcastle upon Tyne, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.768491.

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Aims: 1) To measure automatically in stroke survivors and neurologically intact adults, learning, inter-manual transfer (ImT) and retention of learning (Ret.) of a task requiring two sequential actions embedded with-in a video game. 2) To assess the effect of age and side of stroke on learning, ImT, and Ret. of a motor task consisting of two sequentially linked actions. Participants: All participants were right hand dominant and included: A) 112 neurologically intact adults comprising: 72 younger adults (41 females), mean±SD age, 27.06±4.8 years, range 20-36 years and 40 older adults (26 femal
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Xue, Yongjian. "Dynamic Transfer Learning for One-class Classification : a Multi-task Learning Approach." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0006.

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Le but de cette thèse est de minimiser la perte de performance d'un système de détection lorsqu'il rencontre un changement de distribution de données à la suite d’un événement connu (maintenance, ajout de capteur etc.). L'idée est d'utiliser l'approche d'apprentissage par transfert pour exploiter l'information apprise avant l’événement pour adapter le détecteur au système modifié. Un modèle d'apprentissage multitâche est proposé pour résoudre ce problème. Il utilise un paramètre pour équilibrer la quantité d'informations apportées par l'ancien système par rapport au nouveau. Ce modèle est form
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43

Wilde, Heather Jo. "Proportional and non-proportional transfer of movement sequences." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/3082.

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The ability of spatial transfer to occur in movement sequences is reflected upon in theoretical perspectives, but limited research has been done to verify to what extent spatial characteristics of a sequential learning task occur. Three experiments were designed to determine participants’ ability to transfer a learned movement sequence to new spatial locations. A 16-element dynamic arm movement sequence was used in all experiments. The task required participants to move a horizontal lever to sequentially projected targets. Experiment 1 included 2 groups. One group practiced a pattern in which
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Lundström, Dennis. "Data-efficient Transfer Learning with Pre-trained Networks." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138612.

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Deep learning has dominated the computer vision field since 2012, but a common criticism of deep learning methods is their dependence on large amounts of data. To combat this criticism research into data-efficient deep learning is growing. The foremost success in data-efficient deep learning is transfer learning with networks pre-trained on the ImageNet dataset. Pre-trained networks have achieved state-of-the-art performance on many tasks. We consider the pre-trained network method for a new task where we have to collect the data. We hypothesize that the data efficiency of pre-trained networks
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Wright, Michael A. E. "Supporting the transfer of learning of freehand gestures." Thesis, University of Bath, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.665410.

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Freehand gestural interaction, that is gestures performed mid air without holding an input device or wearing markers for tracking, are increasingly being used as an interaction technique for a range of devices and applications. Unlike traditional point-and-click interfaces, gestural interfaces typically provide the user with different freehand gestures for different tasks. For example, whereas opening a music player, selecting a song and moving forward in a playlist are typically accomplished using a series of mouse clicks in a desktop environment, gestural interfaces might provide the user wi
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Zhang, Yuan Ph D. Massachusetts Institute of Technology. "Transfer learning for low-resource natural language analysis." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108847.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<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 131-142).<br>Expressive machine learning models such as deep neural networks are highly effective when they can be trained with large amounts of in-domain labeled training data. While such annotations may not be readily avail
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Robotti, Odile Paola. "Transfer of learning in binary decision making problems." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1445033/.

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Transfer, the use of acquired knowledge, skills and abilities across tasks and contexts, is a key and elusive goal of learning. Most evidence available in literature is based on a limited number of tasks, predominantly open-ended problems, game-like problems and taught school subjects (e.g. maths, physics, algebra). It is not obvious that findings from this work can be extended to the domain of decision making problems. This thesis, which aims to broaden the understanding of enhancing and limiting factors of transfer, examines transfer of binary decision making problems (analogs of the Monty H
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Romera, Paredes B. "Multitask and transfer learning for multi-aspect data." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1457869/.

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Supervised learning aims to learn functional relationships between inputs and outputs. Multitask learning tackles supervised learning tasks by performing them simultaneously to exploit commonalities between them. In this thesis, we focus on the problem of eliminating negative transfer in order to achieve better performance in multitask learning. We start by considering a general scenario in which the relationship between tasks is unknown. We then narrow our analysis to the case where data are characterised by a combination of underlying aspects, e.g., a dataset of images of faces, where each f
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Olsson, Anton, and Felix Rosberg. "Domain Transfer for End-to-end Reinforcement Learning." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43042.

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In this master thesis project a LiDAR-based, depth image-based and semantic segmentation image-based reinforcement learning agent is investigated and compared forlearning in simulation and performing in real-time. The project utilize the Deep Deterministic Policy Gradient architecture for learning continuous actions and was designed to control a RC car. One of the first project to deploy an agent in a real scenario after training in a similar simulation. The project demonstrated that with a proper reward function and by tuning driving parameters such as restricting steering, maximum velocity,
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Qiu, David. "Representation and transfer learning using information-theoretic approximations." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127008.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 119-127).<br>Learning informative and transferable feature representations is a key aspect of machine learning systems. Mutual information and Kullback-Leibler divergence are principled and very popular metrics to measure feature relevance and perform distribution matching, respectively. However, clean formulations of machine learning algorithms based on these information-theoretic
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