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Unsupervised Evaluation of Lidar Domain Adaptation
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020In this work, we investigate the potential of latent representations generated by Variational Autoencoders (VAE) to analyze and distinguish between real and synthetic data. Although the details of the domain adaptation task are not the focus of this work, we use the example of simulated lidar data adapted by a generative model to match real lidar data.
Hubschneider, Christian +2 more
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Assisted Unsupervised Domain Adaptation
2023 IEEE International Symposium on Information Theory (ISIT), 2023Cheng Chen +3 more
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Adversarially robust unsupervised domain adaptation
Artificial IntelligencezbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lianghe Shi, Weiwei Liu
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A simple approach for unsupervised domain adaptation
2016 23rd International Conference on Pattern Recognition (ICPR), 2016Domain adaptation (DA) aims to eliminate the difference between the distribution of labeled source domain on which a classifier is trained and that of unlabeled or partly labeled target domain to which the classifier is to be applied. Compared with the semi-supervised domain adaptation where some labeled data from target domain is utilized to help ...
Xifeng Guo 0001, Wei Chen, Jianping Yin
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Distributionally robust unsupervised domain adaptation
Journal of Computational and Applied MathematicszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yibin Wang 0003, Haifeng Wang
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Adaptive Feature Swapping for Unsupervised Domain Adaptation
Proceedings of the 31st ACM International Conference on Multimedia, 2023Junbao Zhuo +4 more
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Pseudo Labels for Unsupervised Domain Adaptation: A Review
Electronics (Switzerland), 2023Yundong Li, Longxia Guo
exaly
Unsupervised variational domain adaptation
Machine LearningYundong Li +3 more
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Joint bi-adversarial learning for unsupervised domain adaptation
Knowledge-Based Systems, 2022Qing Tian
exaly

