Searched for: subject%3A%22Domain%255C+Adaptation%22
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Michielssen, E. (author), Yilmaz, A. (author), Bagci, H. (author), Jin, J. (author)
The analysis of transient scattering from perfectly conducting as well as potentially inhomogeneous penetrable bodies often is effected using marching on in time based time domain integral equation methods. This paper reviews the state of the art in the field, highlighting recent advances that address not only their stability and computational...
conference paper 2006
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Dinh, C. (author)
Spectral imaging has been extensively applied in many fields, including agriculture, environmental monitoring, biomedical diagnostics, etc. Thanks to the advances in sensor technology, spectral imaging systems nowadays provide finer and finer spectral resolution needed to characterize the spectral properties of materials. The high spectral...
doctoral thesis 2013
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Razoux Schultz, Lex (author)
Automated Sentiment Classification (SC) on short text fragments has been an upcoming field of research. Different machine learning techniques and word representation models have proven to be successful in classifying sentiment of opinion expressions in various domains, i.e. different topics or source media. However, when training on a source...
master thesis 2018
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Bormans, R. P.A. (author), Lindenbergh, R.C. (author), Karimi Nejadasl, F. (author)
One of the biggest challenges for an autonomous vehicle (and hence the WEpod) is to see the world as humans would see it. This understanding is the base for a successful and reliable future of autonomous vehicles. Real-world data and semantic segmentation generally are used to achieve full understanding of its surroundings. However, deploying...
journal article 2018
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Kouw, W.M. (author)
Artificial intelligence, and in particular machine learning, is concerned with teaching computer systems to perform tasks. Tasks such as autonomous driving, recognizing tumors in medical images, or detecting suspicious packages in airports. Such systems learn by observing examples, i.e. data, and forming a mathematical description of what types...
doctoral thesis 2018
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Liu, Xin (author)
This work proposes a method for matching images from different domains in an unsupervised manner, and detecting outlier samples in the target domain at the same time. This matching problem is made difficult by i) the different domain images that are related but under different conditions (e.g. photos of the same location captured in different...
master thesis 2018
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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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Guo, Xuqi (author), Yan, F. (author), Pang, Y. (author), Yan, Gaowei (author)
In the operation process of wet ball mill, there are often multi-modal and multi-condition problems. In this paper, a multi-view based domain adaptive extreme learning machine (MVDAELM) was used to measure the mill load. Firstly, the correlation relationship between the load parameters and the two views (vibration and acoustic signals of the...
conference paper 2019
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Liu, X. (author), Khademi, S. (author), van Gemert, J.C. (author)
Cross domain image matching between image collections from different source and target domains is challenging in times of deep learning due to i) limited variation of image conditions in a training set, ii) lack of paired-image labels during training, iii) the existing of outliers that makes image matching domains not fully overlap. To this end,...
conference paper 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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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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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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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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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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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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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
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Svenningsson, P.O. (author), Fioranelli, F. (author), Yarovoy, Alexander (author)
In this paper, the classification of human activity from micro-Doppler spectrograms measured by a radar network is considered. To cope with differences between the training and test datasets due to changes in the set of participants, signal-to-noise ratio and polarimetry, domain adaptation is proposed. To realize this, linear mapping between the...
conference paper 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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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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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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