Searched for: subject%3A%22Domain%255C+Adaptation%22
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Mattar, Avinash (author)
Passive acoustic sensing utilizes the ability of sound to travel beyond the line-of-sight to understand the surroundings. This provides an advantage over the currently used sensors in Intelligent Vehicles that can sense obstacles within their line-of-sight only. Recently, a localization based approach has been implemented to take advantage of...
master thesis 2020
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Datta, Leonid (author)
Training Convolutional Neural Network (CNN) models is difficult when there is a lack of labeled training data and no unlabeled data is available. A popular method for this is domain adaptation where the weights of a pre-trained CNN model are transferred to the problem setup. The model is pre-trained on the same task but in a different domain...
master thesis 2020
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Naseri Jahfari, Arman (author)
Rainfall is increasing in frequency and intensity due to climate change. Hydrological models exist that can report bottlenecks in urban infrastructures. However, these require accurate rainfall estimations with high temporal and spatial resolution. The fulfillment of these requirements is challenged due to high costs. This can be solved with...
master thesis 2019
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Ni, Xianhao (author)
Our research focuses on speech detection from body movements using wearable accelerometer data collected in an in-the-wild mingling event. We aim to explore the nature of the connection between speech and body movements. More specifically, we stress on the person-specificity of speech. Many studies have shown that speech always comes along with...
master thesis 2019
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Li, Jiahui (author)
A cross-domain visual place recognition (VPR) task is proposed in this work, i.e., matching images of the same architectures depicted in different domains. VPR is commonly treated as an image retrieval task, where a query image from an unknown location is matched with relevant instances from geo-tagged gallery database. Different from...
master thesis 2019
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Lengyel, Attila (author)
This work investigates how prior knowledge from physics-based reflection models can be used to improve the performance of semantic segmentation models under an illumination-based domain shift. We implement various color invariants as a preprocessing step and find that CNNs trained on these color invariants get stuck in worse local minima...
master thesis 2019
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Bormans, Robbert (author)
Robot Care Systems (RCS) is involved in the development of the WEpod, an autonomous shuttle which can transfer up to six people. Based on a predefined map of the environment, the shuttle is able to navigate through mixed traffic its perception sensors such as camera, radar and lidar sensors. This study is acquired in collaboration with RCS and...
master thesis 2018
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