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Unsupervised clustering under domain shift (UCDS) studies how to transfer the knowledge from abundant unlabeled data from multiple source domains to learn the representation of the unlabeled data in a target domain.
Direct clustering of a data matrix
John A Hartigan · 1972
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Image segmentation by clustering
Guy Barrett Coleman and Harry C Andrews · 1979
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
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Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure
Marco Saerens, Patrice Latinne, and Christine Decaestecker · 2002
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Dynamic abstraction in reinforcement learning via clustering
Shie Mannor, Ishai Menache, Amit Hoze, and Uri Klein · 2004
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Chinese whispers-an efficient graph clustering algorithm and its application to natural language processing problems
Chris Biemann · 2006
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On the translocation of masses
Leonid V Kantorovich · 2006
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
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Domain adaptation with structural correspondence learning
John Blitzer, Ryan McDonald, and Fernando Pereira · 2006
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Learning to recognize activities from the wrong view point
Ali Farhadi and Mostafa Kamali Tabrizi · 2008
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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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Discriminative clustering by regularized information maximization
Andreas Krause, Pietro Perona, and Ryan Gomes · 2010
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Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
Yuan Shi and Fei Sha · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Stability and hypothesis transfer learning
Ilja Kuzborskij and Francesco Orabona · 2013
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A clustering-based graph laplacian framework for value function approximation in reinforcement learning
Xin Xu, Zhenhua Huang, Daniel Graves, and Witold Pedrycz · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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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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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor S. Lempitsky · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I. Jordan · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 2016
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
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Unsupervised deep embedding for clustering analysis
Junyuan Xie, Ross Girshick, and Ali Farhadi · 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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Minimal-entropy correlation alignment for unsupervised deep domain adaptation
Pietro Morerio, Jacopo Cavazza, and Vittorio Murino · 2017
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Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017
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Towards k-means-friendly spaces: Simultaneous deep learning and clustering
Bo Yang, Xiao Fu, Nicholas D Sidiropoulos, and Mingyi Hong · 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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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Joint distribution optimal transportation for domain adaptation
Nicolas Courty, Rémi Flamary, Amaury Habrard, and Alain Rakotomamonjy · 2017
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Hypothesis transfer learning via transformation functions
Simon S Du, Jayanth Koushik, Aarti Singh, and Barnabás Póczos · 2017
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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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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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Superpixel-based fast fuzzy c-means clustering for color image segmentation
Tao Lei, Xiaohong Jia, Yanning Zhang, Shigang Liu, Hongying Meng, and Asoke K Nandi · 2018
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Deep adversarial attention alignment for unsupervised domain adaptation: the benefit of target expectation maximization
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Adversarial learning for robust deep clustering
Xu Yang, Cheng Deng, Kun Wei, Junchi Yan, and Wei Liu · 2020
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Deep transformation-invariant clustering
Tom Monnier, Thibault Groueix, and Mathieu Aubry · 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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Universal source-free domain adaptation
Jogendra Nath Kundu, Naveen Venkat, R Venkatesh Babu, et al · 2020
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Domain adaptation with conditional distribution matching and generalized label shift
Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, and Geoff Gordon · 2020
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Guoliang Kang, Liang Zheng, Yan Yan, and Yi Yang · 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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A survey of clustering with deep learning: From the perspective of network architecture
Erxue Min, Xifeng Guo, Qiang Liu, Gen Zhang, Jianjing Cui, and Jun Long · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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A compare-aggregate model with latent clustering for answer selection
Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, and Kyomin Jung · 2019
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Deep multimodal clustering for unsupervised audiovisual learning
Di Hu, Feiping Nie, and Xuelong Li · 2019
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A survey on data-efficient algorithms in big data era
Amina Adadi · 2021
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Fusedream: Training-free text-to-image generation with improved clip+ gan space optimization
Xingchao Liu, Chengyue Gong, Lemeng Wu, Shujian Zhang, Hao Su, and Qiang Liu · 2021
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A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets
Himanshu Mittal, Avinash Chandra Pandey, Mukesh Saraswat, Sumit Kumar, Raju Pal, and Garv Modwel · 2021
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Dqre-scnet: a novel hybrid approach for selecting users in federated learning with deep-q-reinforcement learning based on spectral clustering
Mohsen Ahmadi, Ali Taghavirashidizadeh, Danial Javaheri, Armin Masoumian, Saeid Jafarzadeh Ghoushchi, and Yaghoub Pourasad · 2021
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Multimodal clustering networks for self-supervised learning from unlabeled videos
Brian Chen, Andrew Rouditchenko, Kevin Duarte, Hilde Kuehne, Samuel Thomas, Angie Boggust, Rameswar Panda, Brian Kingsbury, Rogerio Feris, David Harwath, et al · 2021
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Medical imaging deep learning with differential privacy
Alexander Ziller, Dmitrii Usynin, Rickmer Braren, Marcus Makowski, Daniel Rueckert, and Georgios Kaissis · 2021
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Contextual dropout: An efficient sample-dependent dropout module
Xinjie Fan, Shujian Zhang, Korawat Tanwisuth, Xiaoning Qian, and Mingyuan Zhou · 2021
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Self-knowledge distillation with progressive refinement of targets
Kyungyul Kim, ByeongMoon Ji, Doyoung Yoon, and Sangheum Hwang · 2021
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Contrastive fine-grained class clustering via generative adversarial networks
Yunji Kim and Jung-Woo Ha · 2021
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Clustering by maximizing mutual information across views
Kien Do, Truyen Tran, and Svetha Venkatesh · 2021
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Nearest neighbor matching for deep clustering
Zhiyuan Dang, Cheng Deng, Xu Yang, Kun Wei, and Heng Huang · 2021
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Multi-view contrastive graph clustering
Erlin Pan and Zhao Kang · 2021
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You never cluster alone
Yuming Shen, Ziyi Shen, Menghan Wang, Jie Qin, Philip Torr, and Ling Shao · 2021
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Multi-facet clustering variational autoencoders
Fabian Falck, Haoting Zhang, Matthew Willetts, George Nicholson, Christopher Yau, and Chris C Holmes · 2021
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A deep variational approach to clustering survival data
Laura Manduchi, Ričards Marcinkevičs, Michela C Massi, Thomas Weikert, Alexander Sauter, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C Neidert, Marc Pfister, et al · 2021
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Discriminative similarity for data clustering
Yingzhen Yang and Ping Li · 2021
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Domain impression: A source data free domain adaptation method
Vinod K Kurmi, Venkatesh K Subramanian, and Vinay P Namboodiri · 2021
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Sofa: Source-data-free feature alignment for unsupervised domain adaptation
Hao-Wei Yeh, Baoyao Yang, Pong C Yuen, and Tatsuya Harada · 2021
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A prototype-oriented framework for unsupervised domain adaptation
Korawat Tanwisuth, Xinjie Fan, Huangjie Zheng, Shujian Zhang, Hao Zhang, Bo Chen, and Mingyuan Zhou · 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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On universal black-box domain adaptation
Bin Deng, Yabin Zhang, Hui Tang, Changxing Ding, and Kui Jia · 2021
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Distill and fine-tune: Effective adaptation from a black-box source model
Jian Liang, Dapeng Hu, Ran He, and Jiashi Feng · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Towards data-free model stealing in a hard label setting
Sunandini Sanyal, Sravanti Addepalli, and R Venkatesh Babu · 2022
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Representing mixtures of word embeddings with mixtures of topic embeddings
Dongsheng Wang, Dandan Guo, He Zhao, Huangjie Zheng, Korawat Tanwisuth, Bo Chen, and Mingyuan Zhou · 2022
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Allsh: Active learning guided by local sensitivity and hardness
Shujian Zhang, Chengyue Gong, Xingchao Liu, Pengcheng He, Weizhu Chen, and Mingyuan Zhou · 2022
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