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Unsupervised Domain Adaptation (UDA) aims to bridge the gap between a source domain, where labelled data are available, and a target domain only represented with unlabelled data.
Deep batch active learning by diverse, uncertain gradient lower bounds
Ash, J. T.; Zhang, C.; Krishnamurthy, A.; Langford, J.; and Agarwal, A. 2019 · 1906
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Arjovsky, M.; Bottou, L.; Gulrajani, I.; and Lopez-Paz, D. 2019 · 1907
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Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation
Bouvier, V.; Very, P.; Hudelot, C.; and Chastagnol, C. 2019 · 1907
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Geva, M.; Goldberg, Y.; and Berant, J. 2019 · 1908
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The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence
Marcus, G. 2020 · 2002
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Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Combes, R. T. d.; Zhao, H.; Wang, Y.-X.; and Gordon, G. 2020 · 2003
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k-means++: The advantages of careful seeding
Arthur, D.; and Vassilvitskii, S. 2006 · 2006
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Robust Domain Adaptation: Representations, Weights and Inductive Bias
Bouvier, V.; Very, P.; Chastagnol, C.; Tami, M.; and Hudelot, C. 2020 · 2006
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Target Consistency for Domain Adaptation: when Robustness meets Transferability
Ouali, Y.; Bouvier, V.; Tami, M.; and Hudelot, C. 2020 · 2006
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Margin-based active learning for structured output spaces
Roth, D.; and Small, K. 2006 · 2006
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Analysis of representations for domain adaptation
Ben-David, S.; Blitzer, J.; Crammer, K.; and Pereira, F. 2007 · 2007
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Matplotlib: A 2D graphics environment
Hunter, J. D. 2007 · 2007
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Agnostic active learning
Balcan, M.-F.; Beygelzimer, A.; and Langford, J. 2009 · 2009
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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A survey on transfer learning
Pan, S. J.; and Yang, Q. 2009 · 2009
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Dataset shift in machine learning
Quionero-Candela, J.; Sugiyama, M.; Schwaighofer, A.; and Lawrence, N. D. 2009 · 2009
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Active learning literature survey
Settles, B. 2009 · 2009
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A theory of learning from different domains
Ben-David, S.; Blitzer, J.; Crammer, K.; Kulesza, A.; Pereira, F.; and Vaughan, J. W. 2010 · 2010
Cited alongside, same era.
Domain adaptation meets active learning
Rai, P.; Saha, A.; Daumé III, H.; and Venkatasubramanian, S. 2010 · 2010
Cited alongside, same era.
Adapting visual category models to new domains
Saenko, K.; Kulis, B.; Fritz, M.; and Darrell, T. 2010 · 2010
Cited alongside, same era.
Active supervised domain adaptation
Saha, A.; Rai, P.; Daumé, H.; Venkatasubramanian, S.; and DuVall, S. L. 2011 · 2011
Cited alongside, same era.
The NumPy array: a structure for efficient numerical computation
Walt, S. v. d.; Colbert, S. C.; and Varoquaux, G. 2011 · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
Cited alongside, same era.
Unsupervised domain adaptation with residual transfer networks
Long, M.; Zhu, H.; Wang, J.; and Jordan, M. I. 2016 · 2016
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Deep transfer learning with joint adaptation networks
Long, M.; Zhu, H.; Wang, J.; and Jordan, M. I. 2017 · 2017
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Visda: The visual domain adaptation challenge
Peng, X.; Usman, B.; Kaushik, N.; Hoffman, J.; Wang, D.; and Saenko, K. 2017 · 2017
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Active learning for convolutional neural networks: A core-set approach
Sener, O.; and Savarese, S. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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API design for machine learning software: experiences from the scikit-learn project
Buitinck, L.; Louppe, G.; Blondel, M.; Pedregosa, F.; Mueller, A.; Grisel, O.; Niculae, V.; Prettenhofer, P.; Gramfort, A.; Grobler, J.; Layton, R.; VanderPlas, J.; Joly, A.; Holt, B.; and Varoquaux, G. 2013 · 2013
Cited alongside, same era.
Joint transfer and batch-mode active learning
Chattopadhyay, R.; Fan, W.; Davidson, I.; Panchanathan, S.; and Ye, J. 2013 · 2013
Cited alongside, same era.
Theory of disagreement-based active learning
Hanneke, S.; et al. 2014 · 2014
Cited alongside, same era.
Learning and transferring mid-level image representations using convolutional neural networks
Oquab, M.; Bottou, L.; Laptev, I.; and Sivic, J. 2014 · 2014
Cited alongside, same era.
A new active labeling method for deep learning
Wang, D.; and Shang, Y. 2014 · 2014
Cited alongside, same era.
How transferable are features in deep neural networks?
Yosinski, J.; Clune, J.; Bengio, Y.; and Lipson, H. 2014 · 2014
Cited alongside, same era.
Beery, S.; Van Horn, G.; and Perona, P. 2018 · 2018
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Partial adversarial domain adaptation
Cao, Z.; Ma, L.; Long, M.; and Wang, J. 2018 · 2018
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Conditional adversarial domain adaptation
Long, M.; Cao, Z.; Wang, J.; and Jordan, M. I. 2018 · 2018
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Importance weighted adversarial nets for partial domain adaptation
Zhang, J.; Ding, Z.; Li, W.; and Ogunbona, P. 2018 · 2018
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Support and Invertibility in Domain-Invariant Representations
Johansson, F.; Sontag, D.; and Ranganath, R. 2019 · 2019
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Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers
Liu, H.; Long, M.; Wang, J.; and Jordan, M. 2019 · 2019
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PyTorch: An imperative style, high-performance deep learning library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. 2019 · 2019
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Universal domain adaptation
You, K.; Long, M.; Cao, Z.; Wang, J.; and Jordan, M. I. 2019 · 2019
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On Learning Invariant Representations for Domain Adaptation
Zhao, H.; Des Combes, R. T.; Zhang, K.; and Gordon, G. 2019 · 2019
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Active adversarial domain adaptation
Su, J.-C.; Tsai, Y.-H.; Sohn, K.; Liu, B.; Maji, S.; and Chandraker, M. 2020 · 2020
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Domain-adversarial training of neural networks
Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; Marchand, M.; and Lempitsky, V. 2016 · 2030
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