Fetching the paper…
Reading the bibliography…
Domain adaptation is a critical task in machine learning that aims to improve model performance on a target domain by leveraging knowledge from a related source domain.
Frustratingly easy semi-supervised domain adaptation
Hal Daumé III, Abhishek Kumar, and Avishek Saha · 2010
Earlier work this paper cites.
Co-regularization based semi-supervised domain adaptation
Abhishek Kumar, Avishek Saha, and Hal Daume · 2010
Earlier work this paper cites.
Semi-supervised domain adaptation with instance constraints
Jeff Donahue, Judy Hoffman, Erik Rodner, Kate Saenko, and Trevor Darrell · 2013
Earlier work this paper cites.
Semi-supervised domain adaptation on manifolds
Li Cheng and Sinno Jialin Pan · 2014
Earlier work this paper cites.
Semi-supervised domain adaptation with subspace learning for visual recognition
Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, and Tao Mei · 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 March, and Victor Lempitsky · 2016
Earlier work this paper cites.
Deep CORAL: Correlation alignment for deep domain adaptation
Baocheng Sun and Kate Saenko · 2016
Earlier work this paper cites.
Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
Earlier work this paper cites.
Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
Earlier work this paper cites.
Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
Earlier work this paper cites.
Adversarial multiple source domain adaptation
Han Zhao, Shanghang Zhang, Guanhang Wu, José MF Moura, Joao P Costeira, and Geoffrey J Gordon · 2018
Earlier work this paper cites.
Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
Earlier work this paper cites.
Semi-supervised domain adaptation via minimax entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2019
Earlier work this paper cites.
Universal domain adaptation
Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I. Jordan · 2019
Earlier work this paper cites.
On learning invariant representations for domain adaptation
Han Zhao, Remi Tachet Des Combes, Kun Zhang, and Geoffrey Gordon · 2019
Earlier work this paper cites.
Learning to detect open classes for universal domain adaptation
Bo Fu, Zhangjie Cao, Mingsheng Long, and Jianmin Wang · 2020
Cited alongside, same era.
Bidirectional adversarial training for semi-supervised domain adaptation
Pin Jiang, Aming Wu, Yahong Han, Yunfeng Shao, Meiyu Qi, and Bingshuai Li · 2020
Cited alongside, same era.
Attract, perturb, and explore: Learning a feature alignment network for semi-supervised domain adaptation
Taekyung Kim and Changick Kim · 2020
Cited alongside, same era.
Rethinking distributional matching based domain adaptation
Bo Li, Yezhen Wang, Tong Che, Shanghang Zhang, Sicheng Zhao, Pengfei Xu, Wei Zhou, Yoshua Bengio, and Kurt Keutzer · 2020
Cited alongside, same era.
Online meta-learning for multi-source and semi-supervised domain adaptation
Da Li and Timothy Hospedales · 2020
Cited alongside, same era.
Universal domain adaptation through self-supervision
OVANet: One-vs-all network for universal domain adaptation
Kuniaki Saito and Kate Saenko · 2021
Later among the works it cites.
CLDA: Contrastive learning for semi-supervised domain adaptation
Ankit Singh · 2021
Later among the works it cites.
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
Later among the works it cites.
Adamatch: A unified approach to semi-supervised learning and domain adaptation
David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, and Alex Kurakin · 2022
Later among the works it cites.
Unified optimal transport framework for universal domain adaptation
Wanxing Chang, Ye Shi, Hoang Duong Tuan, and Jingya Wang · 2022
Later among the works it cites.
Distilling and refining domain-specific knowledge for semi-supervised domain adaptation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, and Kate Saenko · 2020
Cited alongside, same era.
FixMatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
Cited alongside, same era.
Deep co-training with task decomposition for semi-supervised domain adaptation
Luyu Yang, Yan Wang, Mingfei Gao, Abhinav Shrivastava, Kilian Q. Weinberger, Wei-Lun Chao, and Ser-Nam Lim · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
Surprisingly simple semi-supervised domain adaptation with pretraining and consistency
Samarth Mishra, Kate Saenko, and Venkatesh Saligrama · 2021
Cited alongside, same era.
Multi-view collaborative learning for semi-supervised domain adaptation
Ba Hung Ngo, Ju Hyun Kim, Yeon Jeong Chae, and Sung In Cho · 2021
Cited alongside, same era.
Ju Hyun Kim, Ba Hung Ngo, Jae Hyeon Park, Jung Eun Kwon, Ho Sub Lee, and Sung In Cho · 2022
Later among the works it cites.
Multi-level consistency learning for semi-supervised domain adaptation
Zizheng Yan, Yushuang Wu, Guanbin Li, Yipeng Qin, Xiaoguang Han, and Shuguang Cui · 2022
Later among the works it cites.
A survey on deep semi-supervised learning
Xiangli Yang, Zixing Song, Irwin King, and Zenglin Xu · 2022
Later among the works it cites.
Semi-supervised domain adaptation via sample-to-sample self-distillation
Jeongbeen Yoon, Dahyun Kang, and Minsu Cho · 2022
Later among the works it cites.
Universal domain adaptation from foundation models
Bin Deng and Kui Jia · 2023
Later among the works it cites.
Semi-supervised domain adaptation via prototype-based multi-level learning
Xinyang Huang, Chuang Zhu, and Wenkai Chen · 2023
Later among the works it cites.
Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang-Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabriel Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2023
Later among the works it cites.
Dynamic re-weighting for long-tailed semi-supervised learning
Hanyu Peng, Weiguo Pian, Mingming Sun, and Ping Li · 2023
Later among the works it cites.
Universal domain adaptation via compressive attention matching
Didi Zhu, Yinchuan Li, Junkun Yuan, Zexi Li, Kun Kuang, and Chao Wu · 2023
Later among the works it cites.