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Learning Invariant Representations has been successfully applied for reconciling a source and a target domain for Unsupervised Domain Adaptation.
Analysis of representations for domain adaptation
S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira · 2007
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Correcting sample selection bias by unlabeled data
J. Huang, A. Gretton, K. Borgwardt, B. Schölkopf, and A. J. Smola · 2007
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Covariate shift adaptation by importance weighted cross validation
M. Sugiyama, M. Krauledat, and K.-R. MÞller · 2007
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Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
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Direct importance estimation with model selection and its application to covariate shift adaptation
M. Sugiyama, S. Nakajima, H. Kashima, P. V. Buenau, and M. Kawanabe · 2008
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Semi-supervised learning (chapelle, o. et al., eds.; 2006) [book reviews]
O. Chapelle, B. Scholkopf, and A. Zien, Eds · 2009
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Covariate shift by kernel mean matching
A. Gretton, A. Smola, J. Huang, M. Schmittfull, K. Borgwardt, and B. Schölkopf · 2009
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Domain adaptation: Learning bounds and algorithms
Y. Mansour, M. Mohri, and A. Rostamizadeh · 2009
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A survey on transfer learning
S. J. Pan and Q. Yang · 2009
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Dataset shift in machine learning
J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence · 2009
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A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010
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Adapting visual category models to new domains
K. Saenko, B. Kulis, M. Fritz, and T. Darrell · 2010
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Transfer learning
L. Torrey and J. Shavlik · 2010
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A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
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Domain adaptation under target and conditional shift
K. Zhang, B. Schölkopf, K. Muandet, and Z. Wang · 2013
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Semi-supervised learning of class balance under class-prior change by distribution matching
M. C. Du Plessis and M. Sugiyama · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
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Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Concrete problems in ai safety
D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané · 2016
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
S. Laine and T. Aila · 2016
Cited alongside, same era.
Unsupervised domain adaptation with residual transfer networks
M. Long, H. Zhu, J. Wang, and M. I. Jordan · 2016
Cited alongside, same era.
Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
Cited alongside, same era.
Improving the robustness of deep neural networks via stability training
S. Zheng, Y. Song, T. Leung, and I. Goodfellow · 2016
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Y. Carmon, A. Raghunathan, L. Schmidt, J. C. Duchi, and P. S. Liang · 2019
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Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation
X. Chen, S. Wang, M. Long, and J. Wang · 2019
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Autoaugment: Learning augmentation strategies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2019
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Randaugment: Practical data augmentation with no separate search
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le · 2019
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M. Geva, Y. Goldberg, and J. Berant · 2019
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2017
Cited alongside, same era.
Overlap in observational studies with high-dimensional covariates
A. D’Amour, P. Ding, A. Feller, L. Lei, and J. Sekhon · 2017
Cited alongside, same era.
Deep transfer learning with joint adaptation networks
M. Long, H. Zhu, J. Wang, and M. I. Jordan · 2017
Cited alongside, same era.
Visda: The visual domain adaptation challenge
X. Peng, B. Usman, N. Kaushik, J. Hoffman, D. Wang, and K. Saenko · 2017
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
A. Tarvainen and H. Valpola · 2017
Cited alongside, same era.
Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
Cited alongside, same era.
Deep hashing network for unsupervised domain adaptation
H. Venkateswara, J. Eusebio, S. Chakraborty, and S. Panchanathan · 2017
Cited alongside, same era.
Support and invertibility in domain-invariant representations
F. Johansson, D. Sontag, and R. Ranganath · 2019
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Fast autoaugment
S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim · 2019
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Transferable adversarial training: A general approach to adapting deep classifiers
H. Liu, M. Long, J. Wang, and M. Jordan · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
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Dissecting person re-identification from the viewpoint of viewpoint
X. Sun and L. Zheng · 2019
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Interpolation consistency training for semi-supervised learning
V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz · 2019
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Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation
T.-H. Vu, H. Jain, M. Bucher, M. Cord, and P. Pérez · 2019
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Transferable normalization: Towards improving transferability of deep neural networks
X. Wang, Y. Jin, M. Long, J. Wang, and M. I. Jordan · 2019
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Domain adaptation with asymmetrically-relaxed distribution alignment
Y. Wu, E. Winston, D. Kaushik, and Z. Lipton · 2019
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Unsupervised data augmentation
Q. Xie, Z. Dai, E. Hovy, M.-T. Luong, and Q. V. Le · 2019
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A fourier perspective on model robustness in computer vision
D. Yin, R. G. Lopes, J. Shlens, E. D. Cubuk, and J. Gilmer · 2019
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Universal domain adaptation
K. You, M. Long, Z. Cao, J. Wang, and M. I. Jordan · 2019
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On learning invariant representations for domain adaptation
H. Zhao, R. T. Des Combes, K. Zhang, and G. Gordon · 2019
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Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring
D. Berthelot, N. Carlini, E. D. Cubuk, A. Kurakin, K. Sohn, H. Zhang, and C. Raffel · 2020
Closest in time.
Robust domain adaptation: Representations, weights and inductive bias
V. Bouvier, P. Very, C. Chastagnol, M. Tami, and C. Hudelot · 2020
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Domain adaptation with conditional distribution matching and generalized label shift
R. T. d. Combes, H. Zhao, Y.-X. Wang, and G. Gordon · 2020
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Augmix: A simple data processing method to improve robustness and uncertainty
D. Hendrycks, N. Mu, E. D. Cubuk, B. Zoph, J. Gilmer, and B. Lakshminarayanan · 2020
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The next decade in ai: Four steps towards robust artificial intelligence
G. Marcus · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
K. Sohn, D. Berthelot, C.-L. Li, Z. Zhang, N. Carlini, E. D. Cubuk, A. Kurakin, H. Zhang, and C. Raffel · 2020
Closest in time.