Dissertations / Theses on the topic 'Transfer learning'
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Shell, Jethro. "Fuzzy transfer learning." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/8842.
Full textLu, Ying. "Transfer Learning for Image Classification." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEC045/document.
Full textWhen learning a classification model for a new target domain with only a small amount of training samples, brute force application of machine learning algorithms generally leads to over-fitted classifiers with poor generalization skills. On the other hand, collecting a sufficient number of manually labeled training samples may prove very expensive. Transfer Learning methods aim to solve this kind of problems by transferring knowledge from related source domain which has much more data to help classification in the target domain. Depending on different assumptions about target domain and source domain, transfer learning can be further categorized into three categories: Inductive Transfer Learning, Transductive Transfer Learning (Domain Adaptation) and Unsupervised Transfer Learning. We focus on the first one which assumes that the target task and source task are different but related. More specifically, we assume that both target task and source task are classification tasks, while the target categories and source categories are different but related. We propose two different methods to approach this ITL problem. In the first work we propose a new discriminative transfer learning method, namely DTL, combining a series of hypotheses made by both the model learned with target training samples, and the additional models learned with source category samples. Specifically, we use the sparse reconstruction residual as a basic discriminant, and enhance its discriminative power by comparing two residuals from a positive and a negative dictionary. On this basis, we make use of similarities and dissimilarities by choosing both positively correlated and negatively correlated source categories to form additional dictionaries. A new Wilcoxon-Mann-Whitney statistic based cost function is proposed to choose the additional dictionaries with unbalanced training data. Also, two parallel boosting processes are applied to both the positive and negative data distributions to further improve classifier performance. On two different image classification databases, the proposed DTL consistently out performs other state-of-the-art transfer learning methods, while at the same time maintaining very efficient runtime. In the second work we combine the power of Optimal Transport and Deep Neural Networks to tackle the ITL problem. Specifically, we propose a novel method to jointly fine-tune a Deep Neural Network with source data and target data. By adding an Optimal Transport loss (OT loss) between source and target classifier predictions as a constraint on the source classifier, the proposed Joint Transfer Learning Network (JTLN) can effectively learn useful knowledge for target classification from source data. Furthermore, by using different kind of metric as cost matrix for the OT loss, JTLN can incorporate different prior knowledge about the relatedness between target categories and source categories. We carried out experiments with JTLN based on Alexnet on image classification datasets and the results verify the effectiveness of the proposed JTLN in comparison with standard consecutive fine-tuning. To the best of our knowledge, the proposed JTLN is the first work to tackle ITL with Deep Neural Networks while incorporating prior knowledge on relatedness between target and source categories. This Joint Transfer Learning with OT loss is general and can also be applied to other kind of Neural Networks
Alexander, John W. "Transfer in reinforcement learning." Thesis, University of Aberdeen, 2015. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908.
Full textKiehl, Janet K. "Learning to Change: Organizational Learning and Knowledge Transfer." online version, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1080608710.
Full textJohnson, C. Dustin. "Set-Switching and Learning Transfer." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/psych_hontheses/7.
Full textSkolidis, Grigorios. "Transfer learning with Gaussian processes." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6271.
Full textChen, Xiaoyi. "Transfer Learning with Kernel Methods." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0005.
Full textTransfer Learning aims to take advantage of source data to help the learning task of related but different target data. This thesis contributes to homogeneous transductive transfer learning where no labeled target data is available. In this thesis, we relax the constraint on conditional probability of labels required by covariate shift to be more and more general, based on which the alignment of marginal probabilities of source and target observations renders source and target similar. Thus, firstly, a maximum likelihood based approach is proposed. Secondly, SVM is adapted to transfer learning with an extra MMD-like constraint where Maximum Mean Discrepancy (MMD) measures this similarity. Thirdly, KPCA is used to align data in a RKHS on minimizing MMD. We further develop the KPCA based approach so that a linear transformation in the input space is enough for a good and robust alignment in the RKHS. Experimentally, our proposed approaches are very promising
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.
Full textArnekvist, 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.
Full textFramgångsrik inlärning av beteenden inom ramen för Reinforcement Learning (RL) sker ofta tabula rasa och kräver stora mängder observationer och interaktioner. Att använda RL-algoritmer utanför simulering, i den riktiga världen, är därför ofta inte praktiskt utförbart. Detta har motiverat studier i Transfer Learning för RL, där inlärningen accelereras av erfarenheter från tidigare inlärning av liknande uppgifter. I denna licentiatuppsats utforskar jag hur vi kan vi kan åstadkomma transfer från en enklare manipulationspolicy, till en större samling omarrangeringsproblem. Jag fortsätter sedan med att beskriva hur vi kan modellera hur olika inlärningsproblem skiljer sig åt med hjälp av en lågdimensionell parametrisering, och på så vis effektivisera inlärningen av nya problem. Beroendet av bra funktionsapproximation är ibland problematiskt, särskilt inom RL där statistik om målvariabler inte är kända i förväg. Jag presenterar därför slutligen observationer, och förklaringar, att små varianser för målvariabler tillsammans med momentum-optimering leder till dying ReLU.
QC 20200819
Mare, Angelique. "Motivators of learning and learning transfer in the workplace." Diss., University of Pretoria, 2015. http://hdl.handle.net/2263/52441.
Full textMini Dissertation (MBA)--University of Pretoria, 2015.
pa2016
Gordon Institute of Business Science (GIBS)
MBA
Unrestricted
Redko, Ievgen. "Nonnegative matrix factorization for transfer learning." Thesis, Sorbonne Paris Cité, 2015. http://www.theses.fr/2015USPCD059.
Full textThe ability of a human being to extrapolate previously gained knowledge to other domains inspired a new family of methods in machine learning called transfer learning. Transfer learning is often based on the assumption that objects in both target and source domains share some common feature and/or data space. If this assumption is false, most of transfer learning algorithms are likely to fail. In this thesis we propose to investigate the problem of transfer learning from both theoretical and applicational points of view.First, we present two different methods to solve the problem of unsuper-vised transfer learning based on Non-negative matrix factorization tech-niques. First one proceeds using an iterative optimization procedure that aims at aligning the kernel matrices calculated based on the data from two tasks. Second one represents a linear approach that aims at discovering an embedding for two tasks that decreases the distance between the cor-responding probability distributions while preserving the non-negativity property.We also introduce a theoretical framework based on the Hilbert-Schmidt embeddings that allows us to improve the current state-of-the-art theo-retical results on transfer learning by introducing a natural and intuitive distance measure with strong computational guarantees for its estimation. The proposed results combine the tightness of data-dependent bounds de-rived from Rademacher learning theory while ensuring the efficient esti-mation of its key factors.Both theoretical contributions and the proposed methods were evaluated on a benchmark computer vision data set with promising results. Finally, we believe that the research direction chosen in this thesis may have fruit-ful implications in the nearest future
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.
Full textRecent years have seen a renewed interest in the potential of analogy detection and analogical inference, with successful applications in Machine Learning (ML) to the retrieval and generation of images, of text, and structured data such as knowledge graphs, but also the detection of relations between and within images, texts, and structured data. While some of those works are based on an intuitive understanding of analogy, significant effort has been made since the antiquity to define analogies as accurately as possible. Analogies are a key component of human cognition, and can be viewed as an abstraction mechanism that identifies similarities and differences between different situations, and as a reasoning tool to adapt known solutions to new situation. When reasoning by analogy, the goal is to adapt the solution of a known or source problem, which is sufficiently similar to the actual or target problem. This process involves a transfer between the context of the source problem (the problem and its solution) and the context of the target problem. In the past 50 years, different aspects of the notion of Analogical Proportions (APs) have been explored. An AP is typically composed of four elements, written A:B::C:D when the ratio between A and B is conform with the one between C and D, and this formal tool has been declined to cover numerous different interpretations of what an analogy can be, with any number of elements. In our work, we explore different methods to tackle the detection of APs and solving of analogical equations on different domains, using Deep Learning (DL). In particular, we study word morphology and Target Sense Disambiguation for words in context, two domains for which our model outperform the State of the Art (SotA), as well as in frame semantics, where we obtain encouraging results. We also study CoAT, a Case-Based Reasoning (CBR) system based on analogical transfer. With CoAT, we encounter significant success in the measure of case competence and in the task of case base compression
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.
Full textMallia, Gorg. "Transfer of learning from literature lessons." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274972.
Full textQuattoni, Ariadna J. "Transfer learning algorithms for image classification." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53294.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 124-128).
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 use the structure learning framework (Ando and Zhang, 2005) to learn efficient image representations from unlabeled images annotated with meta-data. In the second part we present a joint sparsity transfer algorithm for image classification. Our algorithm is based on the observation that related categories might be learnable using only a small subset of shared relevant features. To find these features we propose to train classifiers jointly with a shared regularization penalty that minimizes the total number of features involved in the approximation. To solve the joint sparse approximation problem we develop an optimization algorithm whose time and memory complexity is O(n log n) with n being the number of parameters of the joint model. We conduct experiments on news-topic and keyword prediction image classification tasks. We test our method in two settings: a transfer learning and multitask learning setting and show that in both cases leveraging knowledge from related categories can improve performance when training data per category is scarce. Furthermore, our results demonstrate that our model can successfully recover jointly sparse solutions.
by Ariadna Quattoni.
Ph.D.
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.
Full textFarajidavar, Nazli. "Transductive transfer learning for computer vision." Thesis, University of Surrey, 2015. http://epubs.surrey.ac.uk/807998/.
Full textJamil, 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.
Full textCOVID-19 är för närvarande en pågående pandemi och det är en stor efterfrågan på tester, Vilket har lett till att resurserna på sjukhusen inte räcker till. I syfte att öka effektiviteten för COVID-19 tester kan datorsynbaserade system användas. En datorsynsbaserad klassificerare kräver en stor uppsättning träningsdata för att kunna skapa en noggrann och pålitlig modell, vilket för närvarande inte är tillgängligt eftersom sjukdomen endast har existerat i några månader. Diverse modeller används inom sjukvårdssektorn för klassificering av olika sjukdomar. Klassificering av lunginflammationsfall med hjälp av röntgenbilder är ett av de områden där modeller används. Modellerna har uppnått tillräckligt hög noggrannhet för att kunna användas på patienter [18]. Eftersom datamängden är begränsad för identifiering av COVID-19 utvärderar detta arbete nyttan med att använda överföringsinlärning i syfte att förbättra prestandan i COVID-19-detekteringsmodeller. Genom att använda Lunginflammations bilder som en bas för extraktion av attribut, är målet att generera en COVID-19 klassificerare genom överföringsinlärning. Med användning av denna metod uppnåddes en noggrannhet på 97 % jämfört med den ursprungliga noggrannheten på 32 % när överföringsinlärning inte användes.
Mendoza-Schrock, Olga L. "Diffusion Maps and Transfer Subspace Learning." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1503964976467066.
Full textKumar, Sharad. "Localizing Little Landmarks with Transfer Learning." PDXScholar, 2019. https://pdxscholar.library.pdx.edu/open_access_etds/4827.
Full textDaniel, Filippo <1995>. "Transfer learning with generative adversarial networks." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/16989.
Full textBose, Tulika. "Transfer learning for abusive language detection." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0019.
Full textThe proliferation of social media, despite its multitude of benefits, has led to the increased spread of abusive language. Such language, being typically hurtful, toxic, or prejudiced against individuals or groups, requires timely detection and moderation by online platforms. Deep learning models for detecting abusive language have displayed great levels of in-corpus performance but underperform substantially outside the training distribution. Moreover, they require a considerable amount of expensive labeled data for training.This strongly encourages the effective transfer of knowledge from the existing annotated abusive language resources that may have different distributions to low-resource corpora. This thesis studies the problem of transfer learning for abusive language detection and explores various solutions to improve knowledge transfer in cross-corpus scenarios.First, we analyze the cross-corpus generalizability of abusive language detection models without accessing the target during training. We investigate if combining topic model representations with contextual representations can improve generalizability. The association of unseen target comments with abusive language topics in the training corpus is shown to provide complementary information for a better cross-corpus transfer.Secondly, we explore Unsupervised Domain Adaptation (UDA), a type of transductive transfer learning, with access to the unlabeled target corpus. Some popular UDA approaches from sentiment classification are analyzed for cross-corpus abusive language detection. We further adapt a BERT model variant to the unlabeled target using the Masked Language Model (MLM) objective. While the latter improves the cross-corpus performance, the other UDA methods perform sub-optimally. Our analysis reveals their limitations and emphasizes the need for effective adaptation methods suited to this task.As our third contribution, we propose two DA approaches using feature attributions, which are post-hoc model explanations. Particularly, the problem of spurious corpus-specific correlations is studied that restrict the generalizability of classifiers for detecting hate speech, a sub-category of abusive language. While the previous approaches rely on a manually curated list of terms, we automatically extract and penalize the terms causing spurious correlations. Our dynamic approaches improve the cross-corpus performanceover previous works both independently and in combination with pre-defined dictionaries.Finally, we consider transferring knowledge from a resource-rich source to a low-resource target with fewer labeled instances, across different online platforms. A novel training strategy is proposed, which allows flexible modeling of the relative proximity of neighbors retrieved from the resource-rich corpus to learn the amount of transfer. We incorporate neighborhood information with Optimal Transport that permits exploitingthe embedding space geometry. By aligning the joint embedding and label distributions of neighbors, substantial improvements are obtained in low-resource hate speech corpora
Choi, Jin-Woo. "Action Recognition with Knowledge Transfer." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/101780.
Full textDoctor of Philosophy
Recent progress on deep learning has shown remarkable action recognition performance. The remarkable performance is often achieved by transferring the knowledge learned from existing large-scale data to the small-scale data specific to applications. However, existing action recog- nition models do not always work well on new tasks and datasets because of the following two problems. i) Current action recognition datasets have a spurious correlation between action types and background scene types. The models trained on these datasets are biased towards the scene instead of focusing on the actual action. This scene bias leads to poor performance on the new datasets and tasks. ii) Directly testing the model trained on the source data on the target data leads to poor performance as the source, and target distributions are different. Fine-tuning the model on the target data can mitigate this issue. However, manual labeling small-scale target videos is labor-intensive. In this dissertation, I propose solutions to these two problems. To tackle the first problem, I propose to learn scene-invariant action representations to mitigate background scene- biased human action recognition models for the first problem. Specifically, the proposed method learns representations that cannot predict the scene types and the correct actions when there is no evidence. I validate the proposed method's effectiveness by transferring the pre-trained model to multiple action understanding tasks. The results show consistent improvement over the baselines for every task and dataset. To handle the second problem, I formulate human action recognition as an unsupervised learning problem on the target data. In this setting, we have many labeled videos as source data and unlabeled videos as target data. We can use already existing labeled video datasets as source data in this setting. The task is to align the source and target feature distributions so that the learned model can generalize well on the target data. I propose 1) aligning the more important temporal part of each video and 2) encouraging the model to focus on action, not the background scene. The proposed method is simple and intuitive while achieving state-of-the-art performance without training on a lot of labeled target videos. I relax the unsupervised target data setting to a sparsely labeled target data setting. Here, we have many labeled videos as source data and sparsely labeled videos as target data. The setting is practical as sometimes we can afford a little bit of cost for labeling target data. I propose multiple video data augmentation methods to inject color, spatial, temporal, and scene invariances to the action recognition model in this setting. The resulting method shows favorable performance on the public benchmarks.
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/.
Full textPettersson, 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.
Full textAndersen, 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.
Full textDiabetisk näthinnesjukdom (DR) är en komplikation av diabetes och är en sjukdom som påverkar ögonen. Det är en av de största orsakerna till blindhet i västvärlden. Allt eftersom antalet människor med diabetes ökar, ökar även antalet med diabetisk näthinnesjukdom. Detta ställer högre krav på att bättre och effektivare resurser utvecklas för att kunna upptäcka sjukdomen i ett tidigt stadie, vilket är en förutsättning för att förhindra vidareutveckling av sjukdomen som i slutändan kan resultera i blindhet, och att vidare behandling av sjukdomen effektiviseras. Här spelar datorstödd diagnostik en viktig roll. Syftet med denna studie är att undersöka hur ett faltningsnätverk, tillsammans med överföringsinformation, kan prestera när det tränas för multiklass gradering av diabetisk näthinnesjukdom. För att göra detta användes ett färdigbyggt och färdigtränat faltningsnätverk, byggt i Keras, för att fortsättningsvis tränas och finjusteras i Tensorflow på ett 5-klassigt DR dataset. Totalt tjugo träningssessioner genomfördes och noggrannhet, sensitivitet och specificitet utvärderades i varje sådan session. Resultat visar att de uppnådda noggranheterna låg inom intervallet 35% till 48.5%. Den genomsnittliga testsensitiviteten för klass 0, 1, 2, 3 och 4 var 59.7%, 0.0%, 51.0%, 38.7% respektive 0.8%. Vidare uppnåddes en genomsnittlig testspecificitet för klass 1, 2, 3 och 4 på 77.8%, 100.0%, 62.4%, 80.2% respektive 99.7%. Den genomsnittliga sensitiviteten på 0.0% samt den genomsnittliga specificiteten på 100.0% för klass 1 (mild DR) erhölls eftersom CNN modellen aldrig förutsåg denna klass.
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.
Full textDoctor of Philosophy
Toll, Debora K. "The transfer of learning: Employees' lived experiences." Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/29178.
Full textMasko, 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.
Full textDetta examensarbete är en empirisk studie på överföringsträning som ramverk för kalibrering av neurala faltningsnätverks (CNN)-baserade bildbaserad blickapproximationsmodeller. En datamängd på omkring 1 900 000 ögonrandsbilder fördelat över 1682 personer används för att träna och bedöma flertalet blickapproximationsmodeller. Varje modell tränas inledningsvis på all träningsdata, vilket resulterar i generiska modeller. Modellerna kalibreras därefter för vardera testperson med testpersonens kalibreringsdata via överföringsträning genom anpassning av de sista lagren av nätverket. Med överföringsträning observeras en minskning av felet mätt som eukilidskt avstånd för de generiska modellerna inom 12-21%, vilket motsvarar de bästa nuvarande modellerna. För den bäst presterande kalibrerade modellen uppmäts medelfelet 29,53mm och medianfelet 22,77mm. Dock leder kalibrering av regionella sannolikhetsbaserade blickapproximationsmodeller till en försämring av prestanda jämfört med de generiska modellerna. Slutsatsen är att överföringsträning är en legitim kalibreringsansats för att förbättra prestanda hos CNN-baserade bildbaserad blickapproximationsmodeller.
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.
Full textKodirov, Elyor. "Cross-class transfer learning for visual data." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31852.
Full textBoyer, Sebastien (Sebastien Arcario). "Transfer learning for predictive models in MOOCs." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104832.
Full textThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 85-87).
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, they must perform well on a new course from a different discipline, a different context, or even a different MOOC platform. In this thesis, we first investigate whether predictive models "transfer" well to new courses. We then create a framework to evaluate the "transferability" of predictive models. We present methods for overcoming the biases introduced by specific courses into the models by leveraging a multi-course ensemble of models. Using 5 courses from edX, we show a predictive model that, when tested on a new course, achieved up to a 6% increase in AUCROC across 90 different prediction problems. We then tested this model on 10 courses from Coursera (a different platform) and demonstrate that this model achieves an AUCROC of 0.8 across these courses for the problem of predicting dropout one week in advance. Thus, the model "transfers" very well.
by Sebastien Boyer.
S.M. in Technology and Policy
S.M.
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.
Full textGrönlund, 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.
Full textSpråkmodeller kan appliceras på en mängd olika uppgifter med bra resultat, men att träna en språkmodell kan dessvärre vara kostsamt både tids- och pengamässigt. Genom att överföra information från en domän till en annan behöver denna kostsamma träningsprocess bara genomföras en gång, och ger således lättare tillgång till dessa modeller. Dagens forskning genomförs främst med engelska som språk vilket således begränsar mängden av färdigtränade modeller på andra språk. Denna rapport utforskar hur mängden data tillgänglig för träning av språkmodeller påverkar resultatet i ett problem gällande attitydanalys av tweets, och utfördes med svenska som språk. Svenska Wikipedia användes för att först träna språkmodellerna som sedan överfördes till en domän bestående av tweets på svenska. Ett flertal språkmodeller tränades med olika mängd data från dessa två domäner för att sedan kunna jämföra deras prestanda. Resultaten visar att överföring av kunskap från Wikipedia till tweets knappt gav upphov till någon förbättring, medan oövervakad träning på tweets förbättrade resultaten markant.
Pang, Jinyong. "Human Activity Recognition Based on Transfer Learning." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7558.
Full textCifka, Ondrej. "Deep learning methods for music style transfer." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT029.
Full textRecently, deep learning methods have enabled transforming musical material in a data-driven manner. The focus of this thesis is on a family of tasks which we refer to as (one-shot) music style transfer, where the goal is to transfer the style of one musical piece or fragment onto another.In the first part of this work, we focus on supervised methods for symbolic music accompaniment style transfer, aiming to transform a given piece by generating a new accompaniment for it in the style of another piece. The method we have developed is based on supervised sequence-to-sequence learning using recurrent neural networks (RNNs) and leverages a synthetic parallel (pairwise aligned) dataset generated for this purpose using existing accompaniment generation software. We propose a set of objective metrics to evaluate the performance on this new task and we show that the system is successful in generating an accompaniment in the desired style while following the harmonic structure of the input.In the second part, we investigate a more basic question: the role of positional encodings (PE) in music generation using Transformers. In particular, we propose stochastic positional encoding (SPE), a novel form of PE capturing relative positions while being compatible with a recently proposed family of efficient Transformers.We demonstrate that SPE allows for better extrapolation beyond the training sequence length than the commonly used absolute PE.Finally, in the third part, we turn from symbolic music to audio and address the problem of timbre transfer. Specifically, we are interested in transferring the timbre of an audio recording of a single musical instrument onto another such recording while preserving the pitch content of the latter. We present a novel method for this task, based on an extension of the vector-quantized variational autoencoder (VQ-VAE), along with a simple self-supervised learning strategy designed to obtain disentangled representations of timbre and pitch. As in the first part, we design a set of objective metrics for the task. We show that the proposed method is able to outperform existing ones
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.
Full textI denna uppsats identifieras ett vanligt problem med algoritmer för omvänd förstärkt inlärning vilket leder till att de blir beräkningstunga. En lösning föreslås som försöker addressera problemet och som kan byggas på i framtiden. Komplexiteten i algoritmer för omvänd förstärkt inlärning ökar på grund av att varje iteration kräver ett så kallat förstärkt inlärnings-steg som har som syfte att utvärdera föregående iteration och guida lärandet. Detta steg tar lång tid att genomföra för problem med stor tillståndsrymd och där många iterationer är nödvändiga. Det har observerats att problemet som löses i detta steg i många fall är väldigt likt det problem som löstes i föregående iteration. Därför är den föreslagna lösningen att använda sig av informationsöverföring för att ta tillvara denna kunskap. I denna uppsats utvärderas olika former av informationsöverföring för vanliga algoritmer för förstärkt inlärning på detta problem. Experiment görs med value iteration och Q-learning som algoritmerna för förstärkt inlärnings-steget. Algoritmerna appliceras på två ruttplanneringsproblem och finner att i båda fallen kan en informationsöverföring förbättra beräkningstider. För value iteration är överföringen enkel att implementera och förstå och visar stora förbättringar i hastighet jämfört med basfallet. För Qlearning har implementationen fler variabler och samtidigt som en förbättring visas så är den inte lika dramatisk som för value iteration. Slutsaterna som dras är att för implementationer av omvänd förstärkt inlärning där value iteration används som algoritm för förstärkt inlärnings-steget så rekommenderas alltid en informationsöverföring medan för implementationer som använder andra algoritmer så rekommenderas troligtvis en överföring men fler experiment skulle behöva utföras.
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.
Full textGroneman, Kathryn Jane. "The Trouble with Transfer." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/2164.
Full textShalabi, 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.
Full textXue, Yongjian. "Dynamic Transfer Learning for One-class Classification : a Multi-task Learning Approach." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0006.
Full textThe aim of this thesis is to minimize the performance loss of a one-class detection system when it encounters a data distribution change. The idea is to use transfer learning approach to transfer learned information from related old task to the new one. According to the practical applications, we divide this transfer learning problem into two parts, one part is the transfer learning in homogenous space and the other part is in heterogeneous space. A multi-task learning model is proposed to solve the above problem; it uses one parameter to balance the amount of information brought by the old task versus the new task. This model is formalized so that it can be solved by classical one-class SVM except with a different kernel matrix. To select the control parameter, a kernel path solution method is proposed. It computes all the solutions along that introduced parameter and criteria are proposed to choose the corresponding optimal solution at given number of new samples. Experiments show that this model can give a smooth transition from the old detection system to the new one whenever it encounters a data distribution change. Moreover, as the proposed model can be solved by classical one-class SVM, online learning algorithms for one-class SVM are studied later in the purpose of getting a constant false alarm rate. It can be applied to the online learning of the proposed model directly
Wilde, Heather Jo. "Proportional and non-proportional transfer of movement sequences." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/3082.
Full textLundström, 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.
Full textWright, 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.
Full textZhang, 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.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 131-142).
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 available for the target task, it is often possible to find labeled data for another related task. The goal of this thesis is to develop novel transfer learning techniques that can effectively leverage annotations in source tasks to improve performance of the target low-resource task. In particular, we focus on two transfer learning scenarios: (1) transfer across languages and (2) transfer across tasks or domains in the same language. In multilingual transfer, we tackle challenges from two perspectives. First, we show that linguistic prior knowledge can be utilized to guide syntactic parsing with little human intervention, by using a hierarchical low-rank tensor method. In both unsupervised and semi-supervised transfer scenarios, this method consistently outperforms state-of-the-art multilingual transfer parsers and the traditional tensor model across more than ten languages. Second, we study lexical-level multilingual transfer in low-resource settings. We demonstrate that only a few (e.g., ten) word translation pairs suffice for an accurate transfer for part-of-speech (POS) tagging. Averaged across six languages, our approach achieves a 37.5% improvement over the monolingual top-performing method when using a comparable amount of supervision. In the second monolingual transfer scenario, we propose an aspect-augmented adversarial network that allows aspect transfer over the same domain. We use this method to transfer across different aspects in the same pathology reports, where traditional domain adaptation approaches commonly fail. Experimental results demonstrate that our approach outperforms different baselines and model variants, yielding a 24% gain on this pathology dataset.
by Yuan Zhang.
Ph. D.
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/.
Full textRomera, Paredes B. "Multitask and transfer learning for multi-aspect data." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1457869/.
Full textOlsson, 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.
Full textQiu, David. "Representation and transfer learning using information-theoretic approximations." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127008.
Full textCataloged from the official PDF of thesis.
Includes bibliographical references (pages 119-127).
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 quantities typically require density estimation, which could be difficult for high dimensional problems. A central theme of this thesis is to translate these formulations into simpler forms that are more amenable to limited data. In particular, we modify local approximations and variational approximations of information-theoretic quantities to propose algorithms for unsupervised and transfer learning. Experiments show that the representations learned by our algorithms perform competitively compared to popular methods that require higher complexity.
by David Qiu.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Holst, Gustav. "Route Planning of Transfer Buses Using Reinforcement Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281286.
Full textInom ruttplanering är målet att erhålla den bästa färdvägen mellan en uppsättning platser, vilket blir en mycket komplicerad uppgift i takt med att antalet platser ökar. Denna studie kommer att behandla problemet gällande ruttplanering av transferbussar och undersöker genomförbarheten av att tillämpa en förstärkningsinlärningsmetod på detta verkliga problem. I nutida forskning har förstärkningsinlärningsmetoder framträtt som ett lovande alternativ till klassiska optimeringsalgoritmer för lösandet av liknande problem. Detta på grund utav deras positiva egenskaper gällande skalbarhet och generalisering. Emellertid har majoriteten av den nämnda forskningen utförts på strikt teoretiska problem. Denna studie implementerar en befintlig förstärkningsinlärningsmodell och anpassar den till att passa problemet med ruttplanering av transferbussar. Modellen tränas för att generera optimerade rutter, gällande tids- och kostnadskonsumtion. Därefter utvärderas rutterna, som genererats av den tränade modellen, mot motsvarande manuellt planerade rutter. Förstärkningsinlärningsmodellen producerar rutter som överträffar de manuellt planerade rutterna med avseende på de båda undersökta mätvärdena. På grund av avgränsningar och antagandet som gjorts under implementeringen anses emellertid de explicita konsumtionsskillnaderna vara lovande men kan inte ses som definitiva resultat. Huvudfyndet är modellens övergripande beteende, vilket antyder en konceptvalidering; förstärkningsinlärningsmodeller är användbara som verktyg i sammanhanget gällande verklig ruttplanering av transferbussar.