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Unsupervised domain adaptation (UDA) aims at learning a machine learning model using a labeled source domain that performs well on a similar yet different, unlabeled target domain.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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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 public domain dataset for human activity recognition using smartphones
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra Perez, 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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A recurrent latent variable model for sequential data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio · 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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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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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Structured inference networks for nonlinear state space models
Rahul Krishnan, Uri Shalit, and David Sontag · 2017
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Variational recurrent adversarial deep domain adaptation
Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, and Yan Liu · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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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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Recurrent neural networks for multivariate time series with missing values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu · 2018
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An interpretable icu mortality prediction model based on logistic regression and recurrent neural networks with lstm units
Wendong Ge, Jin-Won Huh, Yu Rang Park, Jae-Ho Lee, Young-Hak Kim, and Alexander Turchin · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Multi-adversarial domain adaptation
Zhongyi Pei, Zhangjie Cao, Mingsheng Long, and Jianmin Wang · 2018
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Benchmarking deep learning models on large healthcare datasets
Sanjay Purushotham, Chuizheng Meng, Zhengping Che, and Yan Liu · 2018
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A dirt-t approach to unsupervised domain adaptation
Rui Shu, Hung H Bui, Hirokazu Narui, and Stefano Ermon · 2018
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Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study
John R Zech, Marcus A Badgeley, Manway Liu, Anthony B Costa, Joseph J Titano, and Eric Karl Oermann · 2018
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Deep subdomain adaptation network for image classification
Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, and Qing He · 2018
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Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
Cited alongside, same era.
Temporal attentive alignment for large-scale video domain adaptation
Min-Hung Chen, Zsolt Kira, Ghassan AlRegib, Jaekwon Yoo, Ruxin Chen, and Jian Zheng · 2019
Cited alongside, same era.
Unsupervised scalable representation learning for multivariate time series
Jean-Yves Franceschi, Aymeric Dieuleveut, and Martin Jaggi · 2019
Cited alongside, same era.
Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan, Hrant Khachatrian, David C Kale, Greg Ver Steeg, and Aram Galstyan · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Cited alongside, same era.
Contrastive adaptation network for unsupervised domain adaptation
With a little help from my friends: Nearest-neighbor contrastive learning of visual representations
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman · 2021
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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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Robust contrastive learning using negative samples with diminished semantics
Songwei Ge, Shlok Mishra, Chun-Liang Li, Haohan Wang, and David Jacobs · 2021
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Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data
Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu · 2021
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Clocs: Contrastive learning of cardiac signals across space, time, and patients
Dani Kiyasseh, Tingting Zhu, and David A Clifton · 2021
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Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann · 2019
Cited alongside, same era.
Feature robustness in non-stationary health records: Caveats to deployable model performance in common clinical machine learning tasks
Bret Nestor, Matthew BA McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C Hughes, Anna Goldenberg, and Marzyeh Ghassemi · 2019
Cited alongside, same era.
Subject-aware contrastive learning for biosignals
Joseph Y Cheng, Hanlin Goh, Kaan Dogrusoz, Oncel Tuzel, and Erdrin Azemi · 2020
Cited alongside, same era.
Shuffle and attend: Video domain adaptation
Jinwoo Choi, Gaurav Sharma, Samuel Schulter, and Jia-Bin Huang · 2020
Cited alongside, same era.
The myth of generalisability in clinical research and machine learning in health care
Joseph Futoma, Morgan Simons, Trishan Panch, Finale Doshi-Velez, and Leo Anthony Celi · 2020
Cited alongside, same era.
Bootstrap your own latent – a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 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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Attdmm: an attentive deep markov model for risk scoring in intensive care units
Yilmazcan Ozyurt, Mathias Kraus, Tobias Hatt, and Stefan Feuerriegel · 2021
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Contrast and mix: Temporal contrastive video domain adaptation with background mixing
Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, and Abir Das · 2021
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Clda: Contrastive learning for semi-supervised domain adaptation
Ankit Singh · 2021
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Gradient regularized contrastive learning for continual domain adaptation
Shixiang Tang, Peng Su, Dapeng Chen, and Wanli Ouyang · 2021
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Sharing icu patient data responsibly under the society of critical care medicine/european society of intensive care medicine joint data science collaboration: the amsterdam university medical centers database (amsterdamumcdb) example
Patrick J Thoral, Jan M Peppink, Ronald H Driessen, Eric JG Sijbrands, Erwin JO Kompanje, Lewis Kaplan, Heatherlee Bailey, Jozef Kesecioglu, Maurizio Cecconi, Matthew Churpek, et al · 2021
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Unsupervised representation learning for time series with temporal neighborhood coding
Sana Tonekaboni, Danny Eytan, and Anna Goldenberg · 2021
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Calda: Improving multi-source time series domain adaptation with contrastive adversarial learning
Garrett Wilson, Janardhan Rao Doppa, and Diane J Cook · 2021
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Neighborhood contrastive learning applied to online patient monitoring
Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and Gunnar Rätsch · 2021
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Domain adaptation for time series forecasting via attention sharing
Xiaoyong Jin, Youngsuk Park, Danielle Maddix, Hao Wang, and Yuyang Wang · 2022
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Adatime: A benchmarking suite for domain adaptation on time series data
Mohamed Ragab, Emadeldeen Eldele, Wee Ling Tan, Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, and Xiaoli Li · 2022
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Connect, not collapse: Explaining contrastive learning for unsupervised domain adaptation
Kendrick Shen, Robbie M Jones, Ananya Kumar, Sang Michael Xie, Jeff Z HaoChen, Tengyu Ma, and Percy Liang · 2022
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Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion
Ling Yang and Shenda Hong · 2022
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Ts2vec: Towards universal representation of time series
Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu · 2022
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Self-supervised contrastive pre-training for time series via time-frequency consistency
Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik · 2022
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