Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 328 for “"Transfer learning"”.
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Fuzzy Transfer Learning
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 …
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Universal Transfer Learning
Our distance measures and learning algorithms are based on powerful, elegant and beautiful ideas from the field of Algorithmic Information Theory. While developing our transfer learning mechanisms we also derive results that are interesting in and of themselves. We also developed practical …
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Trustworthy transfer learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
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Semi-Supervised Transfer Learning for medical images as an alternative to ImageNet Transfer Learning
One of the main disadvantages of supervised transfer learning is that it necessarily requires a large amount of expensive manually labelled training data. Consequently, even in medical imaging, transfer learning from natural image datasets (such as ImageNet) has become the norm. However, this …
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Bias and fairness in transfer learning
Transfer learning involves using knowledge from one task to improve performance and reduce training time on a related task. However, recent studies highlight a critical issue: the fairness of models trained with transfer learning. One study showed that transfer learning can transfer intentionally …
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Transfer Learning for Accelerated Process Development
… thesis presents a collection of studies on using transfer learning to accelerate various aspects of process development. Part I focuses on reaction optimization, where I propose a benchmarking framework for comparing machine learning strategies for reaction optimization and demonstrate the …
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Transfer Learning For Spoken Language Processing
This thesis develops transfer learning paradigms for spoken language processing applications. In particular, we tackle domain adaptation in the context of Automatic Speech Recognition (ASR) and Cross-Lingual Learning in Automatic Speech Translation (AST). The first part of the thesis develops an …
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Transfer learning algorithms for image classification
… 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) …
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Visual Transfer Learning for Robotic Manipulation
… and expensive. In this thesis, we develop transfer learning algorithms for robotic manipulation in order to reduce the amount of robot-environment interactions needed to adapt to different environments. With real robot hardware, we show that our algorithms enable robots to learn to pick and …
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Foundations of Radio Frequency Transfer Learning
The introduction of Machine Learning (ML) and Deep Learning (DL) techniques into modern radio communications system, a field known as Radio Frequency Machine Learning (RFML), has the potential to provide increased performance and flexibility when compared to traditional signal processing techniques …
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Application-specific transfer learning over edge networks
Transfer learning uses a profound labeled set of data from the source domain to deal with a similar problem for the target domain. Transfer learning provides accurate decision- making when insufficient data samples are available and when building a new prediction model takes more time and effort. …
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Feature-Based Transfer Learning in Novel Systems
In recent years, the transfer learning framework has gained increasing interest in the machine learning community. Fundamentally, this framework aims to train a new system called target domain using existing knowledge from one or more previous system(s) called source domain(s). By extending the …
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Hierarchical transfer learning for small object detection
… proposes two effective solutions: hierarchical transfer learning and slicing-aided hyper inference. Hierarchical transfer learning extends the concept of transfer learning by incorporating multiple steps of knowledge transfer from a large dataset to a target dataset. It leverages intermediate …
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Transfer learning for predictive models in MOOCs
… 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 …
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Deep Neural Networks for Multi-Source Transfer Learning
Transfer learning is gaining incredible attention due to its ability to leverage previously acquired knowledge from source domain to assist in completing a task in a similar target domain. Many existing transfer learning methods deal with single source transfer learning, but rarely consider the …
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Transfer learning and robustness for natural language processing
… (NLP). Driven by the fast development of deep learning, state-of-the-art NLP models have already achieved human-level performance in various large benchmark datasets, such as SQuAD, SNLI, and RACE. However, when these strong models are deployed to real-world applications, they often show poor …
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Transfer learning for low-resource natural language analysis
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 …
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Representation and transfer learning using information-theoretic approximations
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 …
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