Searched for: subject%3A%22Domain%255C+Adaptation%22
(1 - 20 of 36)

Pages

document
Zhu, P. (author)
Questions are critical for information-seeking and learning. Automatic Question Generation (AQG) involves the subjects of Information Retrieval (IR) and Natural Language Processing (NLP), and focuses on automatically creating questions for various applications, subjects which have been studied for decades. In this thesis, we study how to create...
doctoral thesis 2024
document
Beţianu, Miruna (author)
Large language models (LLMs) increasingly serve as the backbone for classifying text associated with distinct domains and simultaneously several labels (classes). When encountering domain shifts, e.g., classifier of movie reviews from IMDb to Rotten Tomatoes, adapting such an LLM-based multi-label classifier is challenging due to incomplete...
master thesis 2023
document
Haarman, Luuk (author)
Convolutional Neural Networks (CNNs) benefit from fine-grained details in high-resolution images, but these images are not always easily available as data collection can be expensive or time-consuming. Transfer learning pre-trains models on data from a related domain before fine-tuning on the main domain, and is a common strategy to deal with...
master thesis 2023
document
van Hoorn, Timo (author)
Sample selection bias occurs when the selected samples in a subset of the original data set follow a different distribution than the samples from the original data set. This type of bias in the training set could result in a classifier being unable to predict samples from a testing data set optimally. Domain adaptation techniques try to adapt...
bachelor thesis 2023
document
Witting, Emiel (author)
Domain adaptation allows machine learning models to perform well in a domain that is different from the available train data. This non-trivial task is approached in many ways and often relies on assumptions about the source (train) and target (test) domains. Unsupervised domain adaptation uses unlabeled target data to mitigate a shift or bias...
bachelor thesis 2023
document
TOCIU, Andrei (author)
Importance weighting is a class of domain adaptation techniques for machine learning, which aims to correct the discrepancy in distribution between the train and test datasets, often caused by sample selection bias. In doing so, it frequently uses unlabeled data from the test set. However, this approach has certain drawbacks: it requires...
bachelor thesis 2023
document
Khan, Zeeshan (author)
Sample selection bias is a well-known problem in machine learning, where the source and target data distributions differ, leading to biased predictions and difficulties in generalization. This bias presents significant challenges for modern machine learning algorithms. To tackle this problem, researchers have focused on developing domain...
bachelor thesis 2023
document
Pasqualetto Cassinis, L. (author), Park, Tae Ha (author), Stacey, Nathan (author), D'Amico, Simone (author), Menicucci, A. (author), Gill, E.K.A. (author), Ahrns, Ingo (author), Sanchez-Gestido, Manuel (author)
This paper introduces an adaptive Convolutional Neural Network (CNN)-based Unscented Kalman Filter for the pose estimation of uncooperative spacecraft. The validation is carried out at Stanford's robotic Testbed for Rendezvous and Optical Navigation on the Satellite Hardware-In-the-loop Rendezvous Trajectories (SHIRT) dataset, which simulates...
journal article 2023
document
Duan, Di (author), Yang, Huanqi (author), Lan, G. (author), Li, Tianxing (author), Jia, Xiaohua (author), Xu, Weitao (author)
This paper presents EMGSense, a low-effort self-supervised domain adaptation framework for sensing applications based on Electromyography (EMG). EMGSense addresses one of the fundamental challenges in EMG cross-user sensing—the significant performance degradation caused by time-varying biological heterogeneity—in a low-effort (data-efficient and...
conference paper 2023
document
Caceres Tocora, Camilo (author)
Semantic segmentation of aerial images is the ability to assign labels to all pixels of an image. It proves to be essential for various applications such as urban planning, agriculture and real-estate analysis. Deep Learning techniques have shown satisfactory results in performing semantic segmentation tasks. Training a deep learning model is an...
master thesis 2022
document
Ju, Nicky (author)
Color Invariant Convolution (CIConv) is a learnable Convolutional Neural Network (CNN) layer that reduces the distribution shift between the source and target set in the CNN under an illumination-based domain shift. We explore the semantic segmentation performance for daynight domain adaptation when using CIConv. We will test this on two...
bachelor thesis 2022
document
Streefkerk, Thomas (author)
CycleGANs [1] and CIConv [2] are both relatively new approaches to their respective applications. For CycleGANs this application is unpaired image-to-image domain adaptation and for CIConv this application is making images more<br/>robust to illumination changes. We investigate whether CycleGANs in combination with CIConv can be used to improve...
bachelor thesis 2022
document
Gioia, Gianpaolo (author)
The possibility to improve an existing method by making (part of) it learnable is explored in this research. The work that this research extends added prior knowledge to a Convolutional Neural Network (CNN) to improve its performance when dealing with an illumination shift. The method used for the preprocessing, is the color invariant. The...
bachelor thesis 2022
document
Yang, Yang (author), Yang, Xiaoyi (author), Sakamoto, Takuya (author), Fioranelli, F. (author), Li, Beichen (author), Lang, Yue (author)
In recent years, gait-based person identification has gained significant interest for a variety of applications, including security systems and public security forensics. Meanwhile, this task is faced with the challenge of disguised gaits. When a human subject changes what he or she is wearing or carrying, it becomes challenging to reliably...
journal article 2022
document
Zhao, Zhijun (author), Yan, Gaowei (author), Ren, Mifeng (author), Cheng, Lan (author), Zhu, Zhujun (author), Pang, Y. (author)
The traditional soft sensor models are based on the independent and identical distribution assumption, which are difficult to adapt to changes in data distribution under multiple operating conditions, resulting in model performance deterioration. The domain adaptive transfer learning methods learn knowledge in different domains by means of...
journal article 2022
document
Pasqualetto Cassinis, L. (author), Menicucci, A. (author), Gill, E.K.A. (author), Ahrns, Ingo (author), Sanchez-Gestido, Manuel (author)
The estimation of the relative pose of an inactive spacecraft by an active servicer spacecraft is a critical task for close-proximity operations, such as In-Orbit Servicing and Active Debris Removal. Among all the challenges, the lack of available space images of the inactive satellite makes the on-ground validation of current monocular...
journal article 2022
document
Dondera, Alin (author)
Moral values play a crucial role in our decision-making process by defining what is right and wrong. With the emergence of political activism and moral discourse on social media, and the latest developments in Natural Language Processing, we are looking at an opportunity to analyze moral values to observe trends as they form. Recent studies have...
bachelor thesis 2021
document
Das, Tuhin (author)
To alleviate lower classification performance on rare classes in imbalanced datasets, a possible solution is to augment the underrepresented classes with synthetic samples. Domain adaptation can be incorporated in a classifier to decrease the domain discrepancy between real and synthetic samples. While domain adaptation is...
bachelor thesis 2021
document
Bons, Wouther (author)
Currently, trained machine learning models are readily available, but their training data might not be (for example due to privacy reasons). This thesis investigates how pre-trained models can be combined for performance on all their source domains, without access to data. This problem is formulated as a Multiple-Source Domain Adaptation (MSA)...
master thesis 2021
document
Kouw, W.M. (author), Loog, M. (author)
Consider a domain-adaptive supervised learning setting, where a classifier learns from labeled data in a source domain and unlabeled data in a target domain to predict the corresponding target labels. If the classifier’s assumption on the relationship between domains (e.g. covariate shift, common subspace, etc.) is valid, then it will usually...
journal article 2021
Searched for: subject%3A%22Domain%255C+Adaptation%22
(1 - 20 of 36)

Pages