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Through prompting, large-scale pre-trained models have become more expressive and powerful, gaining significant attention in recent years.
Semi-supervised learning by entropy minimization
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Prototypical contrastive learning of unsupervised representations
Li, J., Zhou, P., Xiong, C., and Hoi, S. C · 2005
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Optimal transport: old and new , volume 338
Villani, C · 2008
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Discriminative clustering by regularized information maximization
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Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., and Darrell, T · 2010
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Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
Shi, Y. and Sha, F · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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Deep domain confusion: Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., and Darrell, T · 2014
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Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V. S · 2015
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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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Discrete optimal transport: complexity, geometry and applications
Mérigot, Q. and Oudet, E · 2016
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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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Minimal-entropy correlation alignment for unsupervised deep domain adaptation
Morerio, P., Cavazza, J., and Murino, V · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 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
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Partial transfer learning with selective adversarial networks
Cao, Z., Long, M., Wang, J., and Jordan, M. I · 2018
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Unbalanced optimal transport: Dynamic and kantorovich formulations
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Conditional adversarial domain adaptation
Long, M., Cao, Z., Wang, J., and Jordan, M. I · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2018
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Self-labelling via simultaneous clustering and representation learning
Asano, Y. M., Rupprecht, C., and Vedaldi, A · 2019
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Contrastive adaptation network for unsupervised domain adaptation
Kang, G., Jiang, L., Yang, Y., and Hauptmann, A. G · 2019
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Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., and Singh, S · 2020
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Tent: Fully test-time adaptation by entropy minimization
Wang, D., Shelhamer, E., Liu, S., Olshausen, B., and Darrell, T · 2020
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Entropy minimization vs. diversity maximization for domain adaptation
Wu, X., Zhou, Q., Yang, Z., Zhao, C., Latecki, L. J., et al · 2020
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Scaling up visual and vision-language representation learning with noisy text supervision
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q., Sung, Y.-H., Li, Z., and Duerig, T · 2021
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Domain impression: A source data free domain adaptation method
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Transferrable prototypical networks for unsupervised domain adaptation
Pan, Y., Yao, T., Li, Y., Wang, Y., Ngo, C.-W., and Mei, T · 2019
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Moment matching for multi-source domain adaptation
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., and Wang, B · 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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Universal adversarial triggers for attacking and analyzing nlp
Wallace, E., Feng, S., Kandpal, N., Gardner, M., and Singh, S · 2019
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A · 2020
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Kurmi, V. K., Subramanian, V. K., and Namboodiri, V. P · 2021
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The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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A prototype-oriented framework for unsupervised domain adaptation
Tanwisuth, K., Fan, X., Zheng, H., Zhang, S., Zhang, H., Chen, B., and Zhou, M · 2021
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Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation
Yue, X., Zheng, Z., Zhang, S., Gao, Y., Darrell, T., Keutzer, K., and Vincentelli, A. S · 2021
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Exploiting chain rule and Bayes’ theorem to compare probability distributions
Zheng, H. and Zhou, M · 2021
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A survey of vision-language pre-trained models
Du, Y., Liu, Z., Li, J., and Zhao, W. X · 2022
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Learning prototype-oriented set representations for meta-learning
Guo, D., Tian, L., Zhang, M., Zhou, M., and Zha, H · 2022
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Unsupervised prompt learning for vision-language models
Huang, T., Chu, J., and Wei, F · 2022
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Transferability in deep learning: A survey, 2022
Jiang, J., Shu, Y., Wang, J., and Long, M · 2022
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Representing mixtures of word embeddings with mixtures of topic embeddings
Wang, D., Guo, D., Zhao, H., Zheng, H., Tanwisuth, K., Chen, B., and Zhou, M · 2022
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Assaying out-of-distribution generalization in transfer learning
Wenzel, F., Dittadi, A., Gehler, P. V., Simon-Gabriel, C.-J., Horn, M., Zietlow, D., Kernert, D., Russell, C., Brox, T., Schiele, B., et al · 2022
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Allsh: Active learning guided by local sensitivity and hardness
Zhang, S., Gong, C., Liu, X., He, P., Chen, W., and Zhou, M · 2022
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A prototype-oriented clustering for domain shift with source privacy
Tanwisuth, K., Zhang, S., He, P., and Zhou, M · 2023
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