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Mainstream approaches for unsupervised domain adaptation (UDA) learn domain-invariant representations to narrow the domain shift.
Possible generalization of boltzmann-gibbs statistics
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Rademacher and gaussian complexities: Risk bounds and structural results
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Semi-supervised learning by entropy minimization
Grandvalet, Y. and Bengio, Y · 2004
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Semi-supervised self-training of object detection models
Rosenberg, C., Hebert, M., and Schneiderman, H · 2005
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Semi-supervised learning
Chapelle, O., Schölkopf, B., and Zien, A · 2006
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Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
Blitzer, J., Dredze, M., and Pereira, F · 2007
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Dataset Shift in Machine Learning
Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N. D · 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
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A survey on transfer learning
Pan, S. J. and Yang, Q · 2010
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On the hardness of domain adaptation and the utility of unlabeled target samples
Ben-David, S. and Urner, R · 2012
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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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Learning with pseudo-ensembles
Bachman, P., Alsharif, O., and Precup, D · 2014
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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Upper and lower bounds for stochastic processes: modern methods and classical problems , volume 60
Talagrand, M · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., and Jordan, M. I · 2015
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Semi-supervised learning with ladder networks
Rasmus, A., Berglund, M., Honkala, M., Valpola, H., and Raiko, T · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Marchand, M., and Lempitsky, V · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Unsupervised domain adaptation with residual transfer networks
Long, M., Zhu, H., Wang, J., and Jordan, M. I · 2016
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A soft-labeled self-training approach
Mey, A. and Loog, M · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Sajjadi, M., Javanmardi, M., and Tasdizen, T · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Deep transfer learning with joint adaptation networks
Long, M., Zhu, H., Wang, J., and Jordan, M. I · 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
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A. and Valpola, H · 2017
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Adversarial discriminative domain adaptation
Tzeng, E., Hoffman, J., Saenko, K., and Darrell, T · 2017
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Deep hashing network for unsupervised domain adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S · 2017
Cited alongside, same era.
Central moment discrepancy (CMD) for domain-invariant representation learning
Zellinger, W., Grubinger, T., Lughofer, E., Natschläger, T., and Saminger-Platz, S · 2017
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Adversarial category alignment network for cross-domain sentiment classification
Qu, X., Zou, Z., Cheng, Y., Yang, Y., and Zhou, P · 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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Bridging theory and algorithm for domain adaptation
Zhang, Y., Liu, T., Long, M., and Jordan, M · 2019
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On learning invariant representations for domain adaptation
Zhao, H., Combes, R. T. D., Zhang, K., and Gordon, G · 2019
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Confidence regularized self-training
Zou, Y., Yu, Z., Liu, X., Kumar, B. V., and Wang, J · 2019
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Self-training avoids using spurious features under domain shift
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Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing
Albadawy, E. A., Saha, A., and Mazurowski, M. A · 2018
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Self-ensembling for visual domain adaptation
French, G., Mackiewicz, M., and Fisher, M · 2018
Cited alongside, same era.
Cycada: Cycle-consistent adversarial domain adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J., Isola, P., Saenko, K., Efros, A. A., and Darrell, T · 2018
Cited alongside, same era.
Conditional adversarial domain adaptation
Long, M., Cao, Z., Wang, J., and Jordan, M. I · 2018
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S., Ishii, S., and Koyama, M · 2018
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Foundations of machine learning
Mohri, M., Rostamizadeh, A., and Talwalkar, A · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K., Watanabe, K., Ushiku, Y., and Harada, T · 2018
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Chen, Y., Wei, C., Kumar, A., and Ma, T · 2020
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Implicit class-conditioned domain alignment for unsupervised domain adaptation
Jiang, X., Lao, Q., Matwin, S., and Havaei, M · 2020
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Understanding self-training for gradual domain adaptation
Kumar, A., Ma, T., and Liang, P · 2020
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Rethinking distributional matching based domain adaptation
Li, B., Wang, Y., Che, T., Zhang, S., Zhao, S., Xu, P., Zhou, W., Bengio, Y., and Keutzer, K · 2020
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Stochastic classifiers for unsupervised domain adaptation
Lu, Z., Yang, Y., Zhu, X., Liu, C., Song, Y.-Z., and Xiang, T · 2020
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Uncertainty-aware self-training for few-shot text classification
Mukherjee, S. and Awadallah, A · 2020
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Sentry: Selective entropy optimization via committee consistency for unsupervised domain adaptation, 2020
Prabhu, V., Khare, S., Kartik, D., and Hoffman, J · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Sohn, K., Berthelot, D., Carlini, N., Zhang, Z., Zhang, H., Raffel, C. A., Cubuk, E. D., Kurakin, A., and Li, C.-L · 2020
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Class-imbalanced domain adaptation: An empirical odyssey
Tan, S., Peng, X., and Saenko, K · 2020
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Self-training with noisy student improves imagenet classification
Xie, Q., Luong, M. T., Hovy, E., and Le, Q. V · 2020
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Rethinking pre-training and self-training
Zoph, B., Ghiasi, G., Lin, T.-Y., Cui, Y., Liu, H., Cubuk, E. D., and Le, Q · 2020
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A theory of label propagation for subpopulation shift, 2021
Cai, T., Gao, R., Lee, J. D., and Lei, Q · 2021
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Poisoning the unlabeled dataset of semi-supervised learning, 2021
Carlini, N · 2021
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Self-training improves pre-training for natural language understanding
Du, J., Grave, E., Gunel, B., Chaudhary, V., Celebi, O., Auli, M., Stoyanov, V., and Conneau, A · 2021
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Sharpness-aware minimization for efficiently improving generalization
Foret, P., Kleiner, A., Mobahi, H., and Neyshabur, B · 2021
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Self-training converts weak learners to strong learners in mixture models
Frei, S., Zou, D., Chen, Z., and Gu, Q · 2021
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Multi-task self-training for learning general representations
Ghiasi, G., Zoph, B., Cubuk, E. D., Le, Q. V., and Lin, T.-Y · 2021
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Sentry: Selective entropy optimization via committee consistency for unsupervised domain adaptation
Prabhu, V., Khare, S., Kartik, D., and Hoffman, J · 2021
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Strata: Self-training with task augmentation for better few-shot learning
Vu, T., Luong, M.-T., Le, Q. V., Simon, G., and Iyyer, M · 2021
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Theoretical analysis of self-training with deep networks on unlabeled data
Wei, C., Shen, K., Yining, C., and Ma, T · 2021
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In-n-out: Pre-training and self-training using auxiliary information for out-of-distribution robustness
Xie, S. M., Kumar, A., Jones, R., Khani, F., Ma, T., and Liang, P · 2021
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