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We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset.
Gradient-based learning applied to document recognition
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
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Covariate shift adaptation by importance weighted cross validation
Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert MÞller · 2007
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Direct importance estimation with model selection and its application to covariate shift adaptation
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul V Buenau, and Motoaki Kawanabe · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 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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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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Classification in the presence of label noise: a survey
Benoît Frénay and Michel Verleysen · 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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Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 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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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor 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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Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
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Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
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The unreasonable effectiveness of noisy data for fine-grained recognition
Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2017
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Label efficient learning of transferable representations acrosss domains and tasks
Zelun Luo, Yuliang Zou, Judy Hoffman, and Li F Fei-Fei · 2017
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Decoupling” when to update” from” how to update”
Eran Malach and Shai Shalev-Shwartz · 2017
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Deep learning is robust to massive label noise
David Rolnick, Andreas Veit, Serge Belongie, and Nir Shavit · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Adversarial discriminative domain adaptation
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Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, and Li Fei-Fei · 2016
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Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al · 2016
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Open set domain adaptation
P Panareda Busto and Juergen Gall · 2017
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Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning
Weifeng Ge and Yizhou Yu · 2017
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Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A Efros, and Trevor Darrell · 2017
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Learning from noisy labels with distillation
Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, and Li-Jia Li · 2017
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Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation
Hongliang Yan, Yukang Ding, Peihua Li, Qilong Wang, Yong Xu, and Wangmeng Zuo · 2017
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Transfer learning with label noise
Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, and Dacheng Tao · 2017
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On the resistance of neural nets to label noise
Amnon Drory, Shai Avidan, and Raja Giryes · 2018
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Multi-adversarial domain adaptation
Zhongyi Pei, Zhangjie Cao, Mingsheng Long, and Jianmin Wang · 2018
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Importance weighted adversarial nets for partial domain adaptation
Jing Zhang, Zewei Ding, Wanqing Li, and Philip Ogunbona · 2018
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