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Deep networks are prone to performance degradation when there is a domain shift between the source (training) data and target (test) data.
A survey on transfer learning
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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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One-shot adaptation of supervised deep convolutional models
J. Hoffman, E. Tzeng, J. Donahue, Y. Jia, K. Saenko, and Trevor D · 2013
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Revisiting batch normalization for practical domain adaptation
Y. Li, N. Wang, J. Shi, J. Liu, and X. Hou · 2016
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Playing for data: Ground truth from computer games
S. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
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The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez · 2016
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Arbitrary style transfer in real-time with adaptive instance normalization
X. Huang and S. Belongie · 2017
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Deeper, broader and artier domain generalization
D. Li, Y. Yang, Y. Song, and T. Hospedales · 2017
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The mapillary vistas dataset for semantic understanding of street scenes
G. Neuhold, T. Ollmann, Samuel R. Bulò, and P. Kontschieder · 2017
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Visda: The visual domain adaptation challenge
X. Peng, B. Usman, N. Kaushik, J. Hoffman, D. Wang, and Kate Saenko · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Explicit inductive bias for transfer learning with convolutional networks
X. Li, Y. Grandvalet, and F. Davoine · 2018
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Batch-instance normalization for adaptively style-invariant neural networks
H. Nam and H.-E. Kim · 2018
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Two at once: Enhancing learning and generalization capacities via ibn-net
X. Pan, P. Luo, J. Shi, and X. Tang · 2018
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Adapted deep embeddings: A synthesis of methods for k-shot inductive transfer learning
T. R. Scott, K. Ridgeway, and M. Mozer · 2018
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Efficient k-shot learning with regularized deep networks
D. Yoo, H. Fan, V. N. Boddeti, and K. M. Kitani · 2018
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Learning imbalanced datasets with label-distribution-aware margin loss
K. Cao, C. Wei, A. Gaidon, N. Arechiga, and T. Ma · 2019
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Domain-specific batch normalization for unsupervised domain adaptation
W.-G. Chang, T. You, S. Seo, S. Kwak, and B. Han · 2019
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Iterative normalization: Beyond standardization towards efficient whitening
L. Huang, Y. Zhou, F. Zhu, L. Liu, and L. Shao · 2019
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Contrastive adaptation network for unsupervised domain adaptation
G. Kang, L. Jiang, Y. Yang, and A. Hauptmann · 2019
Improving robustness against common corruptions by covariate shift adaptation
S. Schneider, E. Rusak, L. Eck, O. Bringmann, W. Brendel, and M. Bethge · 2020
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Test-time training for out-of-distribution generalization
Y. Sun, X. Wang, Z. Liu, J. Miller, A. A. Efros, and M. Hardt · 2020
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A survey of unsupervised deep domain adaptation
G. Wilson and D. J. Cook · 2020
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BDD100K: A diverse driving dataset for heterogeneous multitask learning
F. Yu, H. Chen, X. Wang, W. Xian, Y. Chen, F. Liu, V. Madhavan, and T. Darrell · 2020
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Contrastive syn-to-real generalization
W. Chen, Z. Yu, S. D. Mello, S. Liu, J. M Alvarez, Z. Wang, and A. Anandkumar · 2021
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Contrastive syn-to-real generalization: Code repository
W. Chen, Z. Yu, S. D. Mello, S. Liu, J. M Alvarez, Z. Wang, and A. Anandkumar · 2021
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DELTA: Deep learning transfer using feature map with attention for convolutional networks
X. Li, H. Xiong, H. Wang, Y. Rao, L. Liu, and J. Huan · 2019
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Switchable whitening for deep representation learning
X. Pan, X. Zhan, J. Shi, X. Tang, and P. Luo · 2019
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Domain adaptation without source data
Y. Kim, S. Hong, D. Cho, H. Park, and P. Panda · 2020
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Wilds: A benchmark of in-the-wild distribution shifts
P. Koh, S. Sagawa, H. Marklund, S. Xie, M. Zhang, A. Balsubramani, W. Hu, M. Yasunaga, R. Phillips, S. Beery, J. Leskovec, A. Kundaje, E. Pierson, S. Levine, C. Finn, and P. Liang · 2020
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Wilds: Code repository
P. Koh, S. Sagawa, H. Marklund, S. Xie, M. Zhang, A. Balsubramani, W. Hu, M. Yasunaga, R. Phillips, S. Beery, J. Leskovec, A. Kundaje, E. Pierson, S. Levine, C. Finn, and P. Liang · 2020
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Towards inheritable models for open-set domain adaptation
J. N. Kundu, N. Venkat, R. Ambareesh, R. M. V., and R. V. Babu · 2020
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Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening
S. Choi, S. Jung, H. Yun, J. Kim, S. Kim, and J. Choo · 2021
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In search of lost domain generalization
I. Gulrajani and D. Lopez-Paz · 2021
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Domain impression: A source data free domain adaptation method
V. Kurmi, Venkatesh K. Subramanian, and Vinay P. Namboodiri · 2021
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Source-free domain adaptation via avatar prototype generation and adaptation
Z. Qiu, Y. Zhang, H. Lin, S. Niu, Y. Liu, Q. Du, and M. Tan · 2021
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Learning a universal template for few-shot dataset generalization
E. Triantafillou, H. Larochelle, R. S. Zemel, and V. Dumoulin · 2021
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Tent: Fully test-time adaptation by entropy minimization
D. Wang, E. Shelhamer, S. Liu, B. Olshausen, and T. Darrell · 2021
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Generalized source-free domain adaptation
S. Yang, Y. Wang, J. Weijer, L. Herranz, and S. Jui · 2021
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Domain generalization with mixstyle
K. Zhou, Y. Yang, Y. Qiao, and T. Xiang · 2021
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Domain generalization with mixstyle: Code repository
K. Zhou, Y. Yang, Y. Qiao, and T. Xiang · 2021
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