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Source free domain adaptation (SFDA) aims to transfer a trained source model to the unlabeled target domain without accessing the source data.
Unsupervised classifiers, mutual information and’phantom targets
John Bridle, Anthony Heading, and David MacKay · 1991
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Active learning: theory and applications to automatic speech recognition
G. Riccardi and D. Hakkani-Tur · 2005
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k-means++: The advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2006
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Correcting sample selection bias by unlabeled data
Jiayuan Huang, Arthur Gretton, Karsten Borgwardt, Bernhard Schölkopf, and Alex Smola · 2006
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A survey on privacy preserving data mining
Jian Wang, Yongcheng Luo, Yan Zhao, and Jiajin Le · 2009
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Multi-class active learning for image classification
Ajay J. Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos · 2009
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Impossibility theorems for domain adaptation
Shai Ben David, Tyler Lu, Teresa Luu, and Dávid Pál · 2010
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Discriminative clustering by regularized information maximization
Andreas Krause, Pietro Perona, and Ryan Gomes · 2010
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Ensemble methods: foundations and algorithms
Zhi-Hua Zhou · 2012
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Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
Yuan Shi and Fei Sha · 2012
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Stability and hypothesis transfer learning
Ilja Kuzborskij and Francesco Orabona · 2013
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A new active labeling method for deep learning
Dan Wang and Yi Shang · 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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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Deep active learning for image classification
Hiranmayi Ranganathan, Hemanth Venkateswara, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Learning discrete representations via information maximizing self-augmented training
Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama · 2017
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Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization
Kamran Ghasedi Dizaji, Amirhossein Herandi, Cheng Deng, Weidong Cai, and Heng Huang · 2017
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Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
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Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2017
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Big data security and privacy protection
Dongpo Zhang · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Gradient-based active learning query strategy for end-to-end speech recognition
Yang Yuan, Soo-Whan Chung, and Hong-Goo Kang · 2019
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Camera on-boarding for person re-identification using hypothesis transfer learning
Sk Miraj Ahmed, Aske R Lejbolle, Rameswar Panda, and Amit K Roy-Chowdhury · 2020
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Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
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Data mining privacy preserving: Research agenda
Inda Kreso, Amra Kapo, and Lejla Turulja · 2021
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Precision health data: Requirements, challenges and existing techniques for data security and privacy
Chandra Thapa and Seyit Camtepe · 2021
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Privacy preserving domain adaptation for semantic segmentation of medical images
Serban Stan and Mohammad Rostami · 2021
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Towards better uncertainty sampling: Active learning with multiple views for deep convolutional neural network
Tao He, Xiaoming Jin, Guiguang Ding, Lan Yi, and Chenggang Yan · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, and Zachary Nado · 2019
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal · 2019
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Semi-supervised domain adaptation via minimax entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2019
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Do we really need to access the source data ? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
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Model adaptation: Unsupervised domain adaptation without source data
Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 2020
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Xianfeng Li, Weijie Chen, Di Xie, Shicai Yang, Peng Yuan, Shiliang Pu, and Yueting Zhuang · 2021
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Source-free domain adaptation for semantic segmentation
Yuang Liu, Wei Zhang, and Jun Wang · 2021
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Adaptive adversarial network for source-free domain adaptation
Haifeng Xia, Handong Zhao, and Zhengming Ding · 2021
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Semi-supervised screening of COVID-19 from positive and unlabeled data with constraint non-negative risk estimator
Zhongyi Han, Rundong He, Tianyang Li, Benzheng Wei, Jian Wang, and Yilong Yin · 2021
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Weather and light level classification for autonomous driving: Dataset, baseline and active learning
Mahesh M Dhananjaya, Varun Ravi Kumar, and Senthil Yogamani · 2021
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Transferable query selection for active domain adaptation
Bo Fu, Zhangjie Cao, Jianmin Wang, and Mingsheng Long · 2021
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Discrepancy-based active learning for domain adaptation
François Deheeger, Mathilde MOUGEOT, Nicolas Vayatis, et al · 2021
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Active domain adaptation via clustering uncertainty-weighted embeddings
Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko, and Judy Hoffman · 2021
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Improving semi-supervised domain adaptation using effective target selection and semantics
Anurag Singh, Naren Doraiswamy, Sawa Takamuku, Megh Bhalerao, Titir Dutta, Soma Biswas, Aditya Chepuri, Balasubramanian Vengatesan, and Naotake Natori · 2021
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Active learning for domain adaptation: An energy-based approach
Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng, and Guoren Wang · 2021
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Exploiting the intrinsic neighborhood structure for source-free domain adaptation
Shiqi Yang, Joost van de Weijer, Luis Herranz, Shangling Jui, et al · 2021
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Generalized source-free domain adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 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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Source-free domain adaptation via avatar prototype generation and adaptation
Zhen Qiu, Yifan Zhang, Hongbin Lin, Shuaicheng Niu, Yanxia Liu, Qing Du, and Mingkui Tan · 2021
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Active learning for domain adaptation: An energy-based approach
Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng, and Guoren Wang · 2021
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Unsupervised batchnorm adaptation (UBNA): A domain adaptation method for semantic segmentation without using source domain representations
Marvin Klingner, Jan-Aike Termöhlen, Jacob Ritterbach, and Tim Fingscheidt · 2022
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