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Test-time adaptation (TTA) addresses distribution shifts for streaming test data in unsupervised settings.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 1903
Earlier work this paper cites.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 1903
Earlier work this paper cites.
Heterogeneous uncertainty sampling for supervised learning
David D Lewis and Jason Catlett · 1994
Earlier work this paper cites.
Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan · 1996
Earlier work this paper cites.
Genetic k-means algorithm
K Krishna and M Narasimha Murty · 1999
Earlier work this paper cites.
Active hidden markov models for information extraction
Tobias Scheffer, Christian Decomain, and Stefan Wrobel · 2001
Earlier work this paper cites.
Representative sampling for text classification using support vector machines
Zhao Xu, Kai Yu, Volker Tresp, Xiaowei Xu, and Jizhi Wang · 2003
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
Earlier work this paper cites.
Detecting change in data streams
Daniel Kifer, Shai Ben-David, and Johannes Gehrke · 2004
Earlier work this paper cites.
Active learning with gaussian processes for object categorization
Ashish Kapoor, Kristen Grauman, Raquel Urtasun, and Trevor Darrell · 2007
Earlier work this paper cites.
Semisupervised svm batch mode active learning with applications to image retrieval
Steven CH Hoi, Rong Jin, Jianke Zhu, and Michael R Lyu · 2009
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Earlier work this paper cites.
Active learning literature survey
Burr Settles · 2009
Earlier work this paper cites.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
Earlier work this paper cites.
Domain adaptation via transfer component analysis
Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang · 2010
Earlier work this paper cites.
Visual recognition and detection under bounded computational resources
Sudheendra Vijayanarasimhan and Ashish Kapoor · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Earlier work this paper cites.
Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Chen Fang, Ye Xu, and Daniel N Rockmore · 2013
Earlier work this paper cites.
Domain-adversarial neural networks
Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, and Mario Marchand · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
A new active labeling method for deep learning
Dan Wang and Yi Shang · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
Earlier work this paper cites.
Visual domain adaptation: A survey of recent advances
Vishal M Patel, Raghuraman Gopalan, Ruonan Li, and Rama Chellappa · 2015
Earlier work this paper cites.
Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Active image segmentation propagation
Suyog Dutt Jain and Kristen Grauman · 2016
Earlier work this paper cites.
Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
Earlier work this paper cites.
Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
Earlier work this paper cites.
Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
Earlier work this paper cites.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
Earlier work this paper cites.
Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
Cited alongside, same era.
Measuring catastrophic forgetting in neural networks
Ronald Kemker, Marc McClure, Angelina Abitino, Tyler Hayes, and Christopher Kanan · 2018
Cited alongside, same era.
How does batch normalization help optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
Cited alongside, same era.
Domain impression: A source data free domain adaptation method
Vinod K Kurmi, Venkatesh K Subramanian, and Vinay P Namboodiri · 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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Adapting off-the-shelf source segmenter for target medical image segmentation
Xiaofeng Liu, Fangxu Xing, Chao Yang, Georges El Fakhri, and Jonghye Woo · 2021
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Invariant causal representation learning for out-of-distribution generalization
Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, and Bernhard Schölkopf · 2021
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Active universal domain adaptation
Xinhong Ma, Junyu Gao, and Changsheng Xu · 2021
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A commute in data: The comma2k19 dataset, 2018
Harald Schafer, Eder Santana, Andrew Haden, and Riccardo Biasini · 2018
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Learning to adapt structured output space for semantic segmentation
Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, and Manmohan Chandraker · 2018
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Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
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Contrastive adaptation network for unsupervised domain adaptation
Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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Multi-anchor active domain adaptation for semantic segmentation
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Active domain adaptation via clustering uncertainty-weighted embeddings
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Tent: Fully test-time adaptation by entropy minimization
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Source-free unsupervised domain adaptation with surrogate data generation
H Yao, Yuhong Guo, and Chunsheng Yang · 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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Test-time batch statistics calibration for covariate shift
Fuming You, Jingjing Li, and Zhou Zhao · 2021
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Unsupervised domain adaptation of black-box source models
Haojian Zhang, Yabin Zhang, Kui Jia, and Lei Zhang · 2021
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Examining and combating spurious features under distribution shift
Chunting Zhou, Xuezhe Ma, Paul Michel, and Graham Neubig · 2021
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Self-supervised noisy label learning for source-free unsupervised domain adaptation
Weijie Chen, Luojun Lin, Shicai Yang, Di Xie, Shiliang Pu, and Yueting Zhuang · 2022
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Proxymix: Proxy-based mixup training with label refinery for source-free domain adaptation
Yuhe Ding, Lijun Sheng, Jian Liang, Aihua Zheng, and Ran He · 2022
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Unsupervised domain adaptation by statistics alignment for deep sleep staging networks
Jiahao Fan, Hangyu Zhu, Xinyu Jiang, Long Meng, Chen Chen, Cong Fu, Huan Yu, Chenyun Dai, and Wei Chen · 2022
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Source-free unsupervised domain adaptation: A survey
Yuqi Fang, Pew-Thian Yap, Weili Lin, Hongtu Zhu, and Mingxia Liu · 2022
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GOOD: A graph out-of-distribution benchmark
Shurui Gui, Xiner Li, Limei Wang, and Shuiwang Ji · 2022
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Dine: Domain adaptation from single and multiple black-box predictors
Jian Liang, Dapeng Hu, Jiashi Feng, and Ran He · 2022
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Zin: When and how to learn invariance without environment partition?
Yong Lin, Shengyu Zhu, Lu Tan, and Peng Cui · 2022
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A source-free domain adaptive polyp detection framework with style diversification flow
Xinyu Liu and Yixuan Yuan · 2022
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Efficient test-time model adaptation without forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan · 2022
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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 · 2022
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Divide to adapt: Mitigating confirmation bias for domain adaptation of black-box predictors
Jianfei Yang, Xiangyu Peng, Kai Wang, Zheng Zhu, Jiashi Feng, Lihua Xie, and Yang You · 2022
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Source-free domain adaptation for real-world image dehazing
Hu Yu, Jie Huang, Yajing Liu, Qi Zhu, Man Zhou, and Feng Zhao · 2022
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A survey on online active learning, 2023
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Planning-oriented autonomous driving
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Salad: Source-free active label-agnostic domain adaptation for classification, segmentation and detection
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Core challenges of social robot navigation: A survey
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Towards stable test-time adaptation in dynamic wild world
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Streaming active learning with deep neural networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, and Jordan T. Ash · 2023
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Adanpc: Exploring non-parametric classifier for test-time adaptation
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