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Unsupervised domain adaptation (UDA) aims to transfer the knowledge learnt from a labeled source domain to an unlabeled target domain.
Unsupervised classifiers, mutual information and’phantom targets’
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Cycada: Cycle-consistent adversarial domain adaptation
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Gradient-based learning applied to document recognition
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Longformer: The long-document transformer
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Integrating structured biological data by kernel maximum mean discrepancy
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Imagenet: A large-scale hierarchical image database
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A survey on transfer learning
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An image is worth 16x16 words: Transformers for image recognition at scale
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Discriminative clustering by regularized information maximization
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Saenko, K.; Kulis, B.; Fritz, M.; and Darrell, T. 2010 · 2010
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Deformable detr: Deformable transformers for end-to-end object detection
Zhu, X.; Su, W.; Lu, L.; Li, B.; Wang, X.; and Dai, J. 2020 · 2010
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Reading digits in natural images with unsupervised feature learning
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End-to-End Video Instance Segmentation with Transformers
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
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Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
Shi, Y.; and Sha, F. 2012 · 2012
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Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
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Generative adversarial networks
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Deep domain confusion: maximizing for domain invariance (2014)
Tzeng, E.; Hoffman, J.; Zhang, N.; Saenko, K.; and Darrell, T. 2014 · 2014
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How transferable are features in deep neural networks?
Yosinski, J.; Clune, J.; Bengio, Y.; and Lipson, H. 2014 · 2014
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Unsupervised domain adaptation by backpropagation
Ganin, Y.; and Lempitsky, V. 2015 · 2015
Deep visual domain adaptation: A survey
Wang, M.; and Deng, W. 2018 · 2018
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Non-local neural networks
Wang, X.; Girshick, R.; Gupta, A.; and He, K. 2018 · 2018
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Transfer learning via learning to transfer
Ying, W.; Zhang, Y.; Huang, J.; and Yang, Q. 2018 · 2018
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Attention augmented convolutional networks
Bello, I.; Zoph, B.; Vaswani, A.; Shlens, J.; and Le, Q. V. 2019 · 2019
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Progressive feature alignment for unsupervised domain adaptation
Chen, C.; Xie, W.; Huang, W.; Rong, Y.; Ding, X.; Huang, Y.; Xu, T.; and Huang, J. 2019 · 2019
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Video action transformer network
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Learning transferable features with deep adaptation networks
Long, M.; Cao, Y.; Wang, J.; and Jordan, M. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Learning discrete representations via information maximizing self-augmented training
Hu, W.; Miyato, T.; Tokui, S.; Matsumoto, E.; and Sugiyama, M. 2017 · 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 · 2017
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Adversarial discriminative domain adaptation
Tzeng, E.; Hoffman, J.; Saenko, K.; and Darrell, T. 2017 · 2017
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Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
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Deep hashing network for unsupervised domain adaptation
Venkateswara, H.; Eusebio, J.; Chakraborty, S.; and Panchanathan, S. 2017 · 2017
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Drop to adapt: Learning discriminative features for unsupervised domain adaptation
Lee, S.; Kim, D.; Kim, N.; and Jeong, S.-G. 2019 · 2019
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Transferable adversarial training: A general approach to adapting deep classifiers
Liu, H.; Long, M.; Wang, J.; and Jordan, M. 2019 · 2019
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Transferable attention for domain adaptation
Wang, X.; Li, L.; Ye, W.; Long, M.; and Wang, J. 2019 · 2019
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End-to-end object detection with transformers
Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; and Zagoruyko, S. 2020 · 2020
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Adversarial-learned loss for domain adaptation
Chen, M.; Zhao, S.; Liu, H.; and Cai, D. 2020 · 2020
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Liang, J.; Hu, D.; and Feng, J. 2020 · 2020
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Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; and Guo, B. 2021 · 2021
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Neimark, D.; Bar, O.; Zohar, M.; and Asselmann, D. 2021 · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wang, W.; Xie, E.; Li, X.; Fan, D.-P.; Song, K.; Liang, D.; Lu, T.; Luo, P.; and Shao, L. 2021 · 2021
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Domain-adversarial training of neural networks
Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; Marchand, M.; and Lempitsky, V. 2016 · 2030
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