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Unsupervised domain adaptation methods aim to generalize well on unlabeled test data that may have a different (shifted) distribution from the training data.
Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals
Ary L. Goldberger, Luis A. N. Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch. Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung-Kang Peng, and H. Eugene Stanley · 2000
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Cross domain distribution adaptation via kernel mapping
Erheng Zhong, Wei Fan, Jing Peng, Kun Zhang, Jiangtao Ren, Deepak Turaga, and Olivier Verscheure · 2009
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Activity recognition using cell phone accelerometers
Jennifer R. Kwapisz, Gary M. Weiss, and Samuel A. Moore · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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A public domain dataset for human activity recognition using smartphones
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge Luis Reyes-Ortiz · 2013
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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition
Allan Stisen, Henrik Blunck, Sourav Bhattacharya, Thor Siiger Prentow, Mikkel Baun Kjærgaard, Anind Dey, Tobias Sonne, and Mads Møller Jensen · 2015
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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification
Christian Lessmeier, James Kuria Kimotho, Detmar Zimmer, and Walter Sextro · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Temporal convolutional networks: A unified approach to action segmentation
Colin Lea, René Vidal, Austin Reiter, and Gregory D. Hager · 2016
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Variational recurrent adversarial deep domain adaptation
Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, and Yan Liu · 2017
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Correlation alignment for unsupervised domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2017
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Asymmetric tri-training for unsupervised domain adaptation
Kuniaki Saito, Yoshitaka Ushiku, and Tatsuya Harada · 2017
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Time series classification with hive-cote: The hierarchical vote collective of transformation-based ensembles
Jason Lines, Sarah Taylor, and Anthony Bagnall · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I. Jordan · 2018
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2018
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A dirt-t approach to unsupervised domain adaptation
Time series encodings with temporal convolutional networks
Markus Thill, Wolfgang Konen, and Thomas Bäck · 2020
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On Minimum Discrepancy Estimation for Deep Domain Adaptation
Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, and Sridha Sridharan · 2020
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Time-series representation learning via temporal and contextual contrasting
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, and Cuntai Guan · 2021
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Modeling temporal patterns with dilated convolutions for time-series forecasting
Yangfan Li, Kenli Li, Cen Chen, Xu Zhou, Zeng Zeng, and Keqin Li · 2021
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Domain adaptation and autoencoder-based unsupervised speech enhancement
Yi Li, Yang Sun, Kirill Horoshenkov, and Syed Mohsen Naqvi · 2021
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Deep subdomain adaptation network for image classification
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Rui Shu, Hung Bui, Hirokazu Narui, and Stefano Ermon · 2018
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Deep learning for time series classification: a review
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller · 2019
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Robust and subject-independent driving manoeuvre anticipation through domain-adversarial recurrent neural networks
Michele Tonutti, Emanuele Ruffaldi, Alessandro Cattaneo, and Carlo Alberto Avizzano · 2019
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Towards accurate model selection in deep unsupervised domain adaptation
Kaichao You, Ximei Wang, Mingsheng Long, and Michael Jordan · 2019
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A systematic study of unsupervised domain adaptation for robust human-activity recognition
Youngjae Chang, Akhil Mathur, Anton Isopoussu, Junehwa Song, and Fahim Kawsar · 2020
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Multi-source deep domain adaptation with weak supervision for time-series sensor data
Garrett Wilson, Janardhan Rao Doppa, and Diane J. Cook · 2020
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A decade survey of transfer learning (2010–2020)
Shuteng Niu, Yongxin Liu, Jian Wang, and Houbing Song · 2020
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Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, and Qing He · 2021
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Adversarial spectral kernel matching for unsupervised time series domain adaptation
Qiao Liu and Hui Xue · 2021
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An attention-based deep learning approach for sleep stage classification with single-channel eeg
Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan · 2021
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Time series domain adaptation via sparse associative structure alignment
Ruichu Cai, Jiawei Chen, Zijian Li, Wei Chen, Keli Zhang, Junjian Ye, Zhuozhang Li, Xiaoyan Yang, and Zhenjie Zhang · 2021
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Quick learning mechanism with cross-domain adaptation for intelligent fault diagnosis
Arun K. Sharma and Nishchal K. Verma · 2022
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Multiple graphs and low-rank embedding for multi-source heterogeneous domain adaptation
Hanrui Wu and Michael K Ng · 2022
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Self-supervised autoregressive domain adaptation for time series data
Mohamed Ragab, Emadeldeen Eldele, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, and Xiaoli Li · 2022
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Conditional contrastive domain generalization for fault diagnosis
Mohamed Ragab, Zhenghua Chen, Wenyu Zhang, Emadeldeen Eldele, Min Wu, Chee-Keong Kwoh, and Xiaoli Li · 2022
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A review of single-source deep unsupervised visual domain adaptation
Sicheng Zhao, Xiangyu Yue, Shanghang Zhang, Bo Li, Han Zhao, Bichen Wu, Ravi Krishna, Joseph E. Gonzalez, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia, and Kurt Keutzer · 2022
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Adast: Attentive cross-domain eeg-based sleep staging framework with iterative self-training
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan · 2023
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