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Domain Adaptation (DA) techniques are important for overcoming the domain shift between the source domain used for training and the target domain where testing takes place.
Catastrophic interference in connectionist networks: The sequential learning problem
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Sleep, learning, and dreams: off-line memory reprocessing
R. Stickgold, J. A. Hobson, R. Fosse, and M. Fosse · 2001
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Coordinated memory replay in the visual cortex and hippocampus during sleep
D. Ji and M. A. Wilson · 2007
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Awake replay of remote experiences in the hippocampus
M. P. Karlsson and L. M. Frank · 2009
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Discriminative clustering by regularized information maximization
R. Gomes, A. Krause, and P. Perona · 2010
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Play it again: reactivation of waking experience and memory
J. O’Neill, B. Pleydell-Bouverie, D. Dupret, and J. Csicsvari · 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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Unbiased look at dataset bias
A. Torralba and A. A. Efros · 2011
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Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
Y. Shi and F. Sha · 2012
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Stability and hypothesis transfer learning
I. Kuzborskij and F. Orabona · 2013
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Continuous manifold based adaptation for evolving visual domains
J. Hoffman, T. Darrell, and K. Saenko · 2014
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Microsoft COCO: Common objects in context
T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 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. Jordan · 2015
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Domain adaptation in the absence of source domain data
B. Chidlovskii, S. Clinchant, and G. Csurka · 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
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A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell · 2016
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Learning discrete representations via information maximizing self-augmented training
W. Hu, T. Miyato, S. Tokui, E. Matsumoto, and M. Sugiyama · 2017
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al · 2017
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Visda: The visual domain adaptation challenge
X. Peng, B. Usman, N. Kaushik, J. Hoffman, D. Wang, and K. Saenko · 2017
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icarl: Incremental classifier and representation learning
S. A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert · 2017
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
Cited alongside, same era.
Adversarial discriminative domain adaptation
Attending to discriminative certainty for domain adaptation
V. K. Kurmi, S. Kumar, and V. P. Namboodiri · 2019
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Distant supervised centroid shift: A simple and efficient approach to visual domain adaptation
J. Liang, R. He, Z. Sun, and T. Tan · 2019
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When does label smoothing help?
R. Müller, S. Kornblith, and G. Hinton · 2019
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Large scale incremental learning
Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, and Y. Fu · 2019
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Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation
R. Xu, G. Li, J. Yang, and L. Lin · 2019
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Adversarial-learned loss for domain adaptation
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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.
Lifelong learning with dynamically expandable networks
J. Yoon, E. Yang, J. Lee, and S. J. Hwang · 2017
Cited alongside, same era.
Central moment discrepancy (cmd) for domain-invariant representation learning
W. Zellinger, T. Grubinger, E. Lughofer, T. Natschläger, and S. Saminger-Platz · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
F. Zenke, B. Poole, and S. Ganguli · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
Cited alongside, same era.
M. Chen, S. Zhao, H. Liu, and D. Cai · 2020
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Gradually vanishing bridge for adversarial domain adaptation
S. Cui, S. Wang, J. Zhuo, C. Su, Q. Huang, and Q. Tian · 2020
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Remind your neural network to prevent catastrophic forgetting
T. L. Hayes, K. Kafle, R. Shrestha, M. Acharya, and C. Kanan · 2020
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Implicit class-conditioned domain alignment for unsupervised domain adaptation
X. Jiang, Q. Lao, S. Matwin, and M. Havaei · 2020
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Heuristic domain adaptation
X. Jin, S. Wang, Y. He, Q. Huang, et al · 2020
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Universal source-free domain adaptation
J. N. Kundu, N. Venkat, R. V. Babu, et al · 2020
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Towards inheritable models for open-set domain adaptation
J. N. Kundu, N. Venkat, A. Revanur, R. V. Babu, et al · 2020
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Hypothesis disparity regularized mutual information maximization
Q. Lao, X. Jiang, and M. Havaei · 2020
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
J. Liang, D. Hu, and J. Feng · 2020
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Unsupervised domain adaptation via structurally regularized deep clustering
H. Tang, K. Chen, and K. Jia · 2020
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Deep high-resolution representation learning for visual recognition
J. Wang, K. Sun, T. Cheng, B. Jiang, C. Deng, Y. Zhao, D. Liu, Y. Mu, M. Tan, X. Wang, et al · 2020
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Unsupervised domain adaptation via structured prediction based selective pseudo-labeling
Q. Wang and T. Breckon · 2020
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Label propagation with augmented anchors: A simple semi-supervised learning baseline for unsupervised domain adaptation
Y. Zhang, B. Deng, K. Jia, and L. Zhang · 2020
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