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Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Grandvalet, Y. and Bengio, Y · 2005
Earlier work this paper cites.
A kernel method for the two-sample-problem
Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., and Smola, A. J · 2007
Earlier work this paper cites.
Correcting sample selection bias by unlabeled data
Huang, J., Gretton, A., Borgwardt, K., Schölkopf, B., and Smola, A. J · 2007
Earlier work this paper cites.
Instance weighting for domain adaptation in nlp
Jiang, J. and Zhai, C · 2007
Earlier work this paper cites.
Cross-domain video concept detection using adaptive svms
Yang, J., Yan, R., and Hauptmann, A. G · 2007
Earlier work this paper cites.
Domain adaptation with multiple sources
Mansour, Y., Mohri, M., and Rostamizadeh, A · 2009
Earlier work this paper cites.
A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. W · 2010
Earlier work this paper cites.
Discriminative clustering by regularized information maximization
Krause, A., Perona, P., and Gomes, R. G · 2010
Earlier work this paper cites.
Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., and Darrell, T · 2010
Earlier work this paper cites.
Domain adaptation for large-scale sentiment classification: a deep learning approach
Glorot, X., Bordes, A., and Bengio, Y · 2011
Earlier work this paper cites.
Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., and Grauman, K · 2012
Earlier work this paper cites.
Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
Shi, Y. and Sha, F · 2012
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Stability and hypothesis transfer learning
Kuzborskij, I. and Orabona, F · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H · 2013
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Transfer feature learning with joint distribution adaptation
Long, M., Wang, J., Ding, G., Sun, J., and Yu, P. S · 2013
Earlier work this paper cites.
Learning categories from few examples with multi model knowledge transfer
Tommasi, T., Orabona, F., and Caputo, B · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., and Jordan, M · 2015
Earlier work this paper cites.
Towards open set deep networks
Bendale, A. and Boult, T. E · 2016
Earlier work this paper cites.
Domain separation networks
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., and Erhan, D · 2016
Cited alongside, same era.
Domain adaptation in the absence of source domain data
Chidlovskii, B., Clinchant, S., and Csurka, G · 2016
Cited alongside, same era.
Deep reconstruction-classification networks for unsupervised domain adaptation
Ghifary, M., Kleijn, W. B., Zhang, M., Balduzzi, D., and Li, W · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Salimans, T. and Kingma, D. P · 2016
Cited alongside, same era.
Return of frustratingly easy domain adaptation
Sun, B., Feng, J., and Saenko, K · 2016
Cited alongside, same era.
Aggregating randomized clustering-promoting invariant projections for domain adaptation
Liang, J., He, R., Sun, Z., and Tan, T · 2018
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Conditional adversarial domain adaptation
Long, M., Cao, Z., Wang, J., and Jordan, M. I · 2018
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Learning differentially private recurrent language models
McMahan, H. B., Ramage, D., Talwar, K., and Zhang, L · 2018
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Source free domain adaptation using an off-the-shelf classifier
Nelakurthi, A. R., Maciejewski, R., and He, J · 2018
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A dirt-t approach to unsupervised domain adaptation
Shu, R., Bui, H. H., Narui, H., and Ermon, S · 2018
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Learning to adapt structured output space for semantic segmentation
Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., and Chandraker, M · 2018
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Practical secure aggregation for privacy-preserving machine learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2017
Cited alongside, same era.
Autodial: Automatic domain alignment layers
Cariucci, F. M., Porzi, L., Caputo, B., Ricci, E., and Bulo, S. R · 2017
Cited alongside, same era.
A comprehensive survey on domain adaptation for visual applications
Csurka, G · 2017
Cited alongside, same era.
Learning discrete representations via information maximizing self-augmented training
Hu, W., Miyato, T., Tokui, S., Matsumoto, E., and Sugiyama, M · 2017
Cited alongside, same era.
Deep transfer learning with joint adaptation networks
Long, M., Zhu, H., Wang, J., and Jordan, M. I · 2017
Cited alongside, same era.
Open set domain adaptation
Panareda Busto, P. and Gall, J · 2017
Cited alongside, same era.
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Learning semantic representations for unsupervised domain adaptation
Xie, S., Zheng, Z., Chen, L., and Chen, C · 2018
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Deep cocktail network: Multi-source unsupervised domain adaptation with category shift
Xu, R., Chen, Z., Zuo, W., Yan, J., and Lin, L · 2018
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Zou, Y., Yu, Z., Vijaya Kumar, B., and Wang, J · 2018
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Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konecny, J., Mazzocchi, S., McMahan, H. B., et al · 2019
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Learning to transfer examples for partial domain adaptation
Cao, Z., You, K., Long, M., Wang, J., and Yang, Q · 2019
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Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation
Chen, X., Wang, S., Long, M., and Wang, J · 2019
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Pseudo-labeling curriculum for unsupervised domain adaptation
Choi, J., Jeong, M., Kim, T., and Kim, C · 2019
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Cluster alignment with a teacher for unsupervised domain adaptation
Deng, Z., Luo, Y., and Zhu, J · 2019
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Distant supervised centroid shift: A simple and efficient approach to visual domain adaptation
Liang, J., He, R., Sun, Z., and Tan, T · 2019
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Separate to adapt: Open set domain adaptation via progressive separation
Liu, H., Cao, Z., Long, M., Wang, J., and Yang, Q · 2019
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When does label smoothing help?
Müller, R., Kornblith, S., and Hinton, G. E · 2019
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Semi-supervised domain adaptation via minimax entropy
Saito, K., Kim, D., Sclaroff, S., Darrell, T., and Saenko, K · 2019
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Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation
Vu, T.-H., Jain, H., Bucher, M., Cord, M., and Pérez, P · 2019
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Transferable normalization: Towards improving transferability of deep neural networks
Wang, X., Jin, Y., Long, M., Wang, J., and Jordan, M. I · 2019
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Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation
Xu, R., Li, G., Yang, J., and Lin, L · 2019
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Federated adversarial domain adaptation
Peng, X., Huang, Z., Zhu, Y., and Saenko, K · 2020
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