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Haidi Badr, Nayer Wanas and Magda Fayek
Unsupervised domain adaptation (UDA) presents a significant challenge in sentiment analysis, especially when faced with differences between source and target domains. This study introduces Weighted Sequential Unsupervised Domain Adaptation (WS-UDA), a no...
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Zhigang Song, Daisong Li, Zhongyou Chen and Wenqin Yang
The unsupervised domain-adaptive vehicle re-identification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain; however, there are knowledge differences between the target domain and the source domain. To mitiga...
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Chengyan Zhong, Guanqiu Qi, Neal Mazur, Sarbani Banerjee, Devanshi Malaviya and Gang Hu
Due to the variation in the image capturing process, the difference between source and target sets causes a challenge in unsupervised domain adaptation (UDA) on person re-identification (re-ID). Given a labeled source training set and an unlabeled target...
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Nicholus Mboga, Stefano D?Aronco, Tais Grippa, Charlotte Pelletier, Stefanos Georganos, Sabine Vanhuysse, Eléonore Wolff, Benoît Smets, Olivier Dewitte, Moritz Lennert and Jan Dirk Wegner
Multitemporal environmental and urban studies are essential to guide policy making to ultimately improve human wellbeing in the Global South. Land-cover products derived from historical aerial orthomosaics acquired decades ago can provide important evide...
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Zhi-Yong Wang and Dae-Ki Kang
CORrelation ALignment (CORAL) is an unsupervised domain adaptation method that uses a linear transformation to align the covariances of source and target domains. Deep CORAL extends CORAL with a nonlinear transformation using a deep neural network and ad...
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Wagner Costa, Déborah Idier, Jérémy Rohmer, Melisa Menendez and Paula Camus
Increasing our capacity to predict extreme storm surges is one of the key issues in terms of coastal flood risk prevention and adaptation. Dynamically forecasting storm surges is computationally expensive. Here, we focus on an alternative data-driven app...
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Imad Eddine Ibrahim Bekkouch, Youssef Youssry, Rustam Gafarov, Adil Khan and Asad Masood Khattak
Domain adaptation is a sub-field of transfer learning that aims at bridging the dissimilarity gap between different domains by transferring and re-using the knowledge obtained in the source domain to the target domain. Many methods have been proposed to ...
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