Fetching the paper…
Reading the bibliography…
Unsupervised domain adaptation (UDA) aims to learn models for a target domain of unlabeled data by transferring knowledge from a labeled source domain.
A database for handwritten text recognition research
Jonathan J. Hull · 1994
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Unsupervised learning of models for recognition
Markus Weber, Max Welling, and Pietro Perona · 2000
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.
A survey on transfer learning
Sinno Jialin Pan, Qiang Yang, et al · 2010
Earlier work this paper cites.
Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Stability and hypothesis transfer learning
Ilja Kuzborskij and Francesco Orabona · 2013
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I. Jordan · 2015
Earlier work this paper cites.
Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2015
Earlier work this paper cites.
Learning with symmetric label noise: The importance of being unhinged
Brendan Van Rooyen, Aditya Krishna Menon, and Robert C Williamson · 2015
Earlier work this paper cites.
Domain adaptation in the absence of source domain data
Boris Chidlovskii, Stephane Clinchant, and Gabriela Csurka · 2016
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.
Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
Earlier work this paper cites.
Mixture proportion estimation via kernel embeddings of distributions
Harish Ramaswamy, Clayton Scott, and Ambuj Tewari · 2016
Cited alongside, same era.
A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
Cited alongside, same era.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
Cited alongside, same era.
Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 2017
Cited alongside, same era.
Bridging theory and algorithm for domain adaptation
Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael Jordan · 2019
Later among the works it cites.
Domain-symmetric networks for adversarial domain adaptation
Yabin Zhang, Hui Tang, Kui Jia, and Mingkui Tan · 2019
Later among the works it cites.
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
Later among the works it cites.
Sigua: Forgetting may make learning with noisy labels more robust
Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, and Masashi Sugiyama · 2020
Later among the works it cites.
A survey of label-noise representation learning: Past, present and future
Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W Tsang, James T Kwok, and Masashi Sugiyama · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Cited alongside, same era.
A theory of learning with corrupted labels
Brendan Van Rooyen and Robert C Williamson · 2017
Cited alongside, same era.
Masking: A new perspective of noisy supervision
Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya Zhang, and Masashi Sugiyama · 2018
Cited alongside, same era.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
Cited alongside, same era.
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
Cited alongside, same era.
Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
Cited alongside, same era.
Yunzhong Hou and Liang Zheng · 2020
Later among the works it cites.
Transfer-learning-library
Mingsheng Long Junguang Jiang, Bo Fu · 2020
Later among the works it cites.
Domain adaptation without source data
Youngeun Kim, Sungeun Hong, Donghyeon Cho, Hyoungseob Park, and Priyadarshini Panda · 2020
Later among the works it cites.
Model adaptation: Unsupervised domain adaptation without source data
Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 2020
Later among the works it cites.
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
Later among the works it cites.
Generative pseudo-label refinement for unsupervised domain adaptation
Pietro Morerio, Riccardo Volpi, Ruggero Ragonesi, and Vittorio Murino · 2020
Later among the works it cites.
Reliable weighted optimal transport for unsupervised domain adaptation
Renjun Xu, Pelen Liu, Liyan Wang, Chao Chen, and Jindong Wang · 2020
Later among the works it cites.
Unsupervised domain adaptation without source data by casting a bait
Shiqi Yang, Yaxing Wang, Joost van de Weijer, and Luis Herranz · 2020
Later among the works it cites.
Time-consistent self-supervision for semi-supervised learning
Tianyi Zhou, Shengjie Wang, and Jeff Bilmes · 2020
Later among the works it cites.
Domain impression: A source data free domain adaptation method
Vinod K Kurmi, Venkatesh K Subramanian, and Vinay P Namboodiri · 2021
Closest in time.
Why did openai choose to release an api instead of open-sourcing the models?
OpenAI · 2021
Closest in time.
Sofa: Source-data-free feature alignment for unsupervised domain adaptation
Hao-Wei Yeh, Baoyao Yang, Pong C Yuen, and Tatsuya Harada · 2021
Closest in time.