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We study the problem of robust domain adaptation in the context of unavailable target labels and source data.
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Nicholas Carlini and David Wagner · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2017
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Aran Nayebi and Surya Ganguli · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Deep hashing network for unsupervised domain adaptation
Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Towards the first adversarially robust neural network model on MNIST
Lukas Schott, Jonas Rauber, Matthias Bethge, and Wieland Brendel · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Domain adaptation without source data
Youngeun Kim, Sungeun Hong, Donghyeon Cho, Hyoungseob Park, and Priyadarshini Panda · 2020
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Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Defense against universal adversarial perturbations
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Adversarial transformation networks: Learning to generate adversarial examples
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
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Defense against adversarial attacks using high-level representation guided denoiser
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Towards imperceptible and robust adversarial example attacks against neural networks
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Towards deep learning models resistant to adversarial attacks
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Jogendra Nath Kundu, Naveen Venkat, R Venkatesh Babu, et al · 2020
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Model adaptation: Unsupervised domain adaptation without source data
Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 2020
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
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Unsupervised domain adaptation in the absence of source data
Roshni Sahoo, Divya Shanmugam, and John Guttag · 2020
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Do adversarially robust imagenet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
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Adversarially robust transfer learning
Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David Jacobs, and Tom Goldstein · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Yu Sun, Xiaolong Wang, Liu Zhuang, John Miller, Moritz Hardt, and Alexei A. Efros · 2020
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A survey of unsupervised deep domain adaptation
Garrett Wilson and Diane J Cook · 2020
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Unsupervised domain adaptation without source data by casting a bait
Shiqi Yang, Yaxing Wang, Joost van de Weijer, and Luis Herranz · 2020
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Domain impression: A source data free domain adaptation method
Vinod K Kurmi, Venkatesh K Subramanian, and Vinay P Namboodiri · 2021
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Adversarially-trained deep nets transfer better
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Tent: Fully test-time adaptation by entropy minimization
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