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Model adaptation aims at solving the domain transfer problem under the constraint of only accessing the pretrained source models.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 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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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Nag: Network for adversary generation
Konda Reddy Mopuri, Utkarsh Ojha, Utsav Garg, and R Venkatesh Babu · 2018
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Generative adversarial perturbations
Omid Poursaeed, Isay Katsman, Bicheng Gao, and Serge Belongie · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
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A new backdoor attack in cnns by training set corruption without label poisoning
Mauro Barni, Kassem Kallas, and Benedetta Tondi · 2019
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
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Defending against universal perturbations with shared adversarial training
Chaithanya Kumar Mummadi, Thomas Brox, and Jan Hendrik Metzen · 2019
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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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Semi-supervised domain adaptation via minimax entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2019
Cited alongside, same era.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
Wanet–imperceptible warping-based backdoor attack
Anh Nguyen and Anh Tran · 2021
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Source-free domain adaptation via avatar prototype generation and adaptation
Zhen Qiu, Yifan Zhang, Hongbin Lin, Shuaicheng Niu, Yanxia Liu, Qing Du, and Mingkui Tan · 2021
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
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Adversarial neuron pruning purifies backdoored deep models
Dongxian Wu and Yisen Wang · 2021
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Adaptive adversarial network for source-free domain adaptation
Haifeng Xia, Handong Zhao, and Zhengming Ding · 2021
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Source data-free domain adaptation of object detector through domain-specific perturbation
Lin Xiong, Mao Ye, Dan Zhang, Yan Gan, Xue Li, and Yingying Zhu · 2021
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Cited alongside, same era.
Model adaptation: Unsupervised domain adaptation without source data
Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 2020
Cited alongside, same era.
Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
Cited alongside, same era.
Input-aware dynamic backdoor attack
Tuan Anh Nguyen and Anh Tran · 2020
Cited alongside, same era.
Universal adversarial training
Ali Shafahi, Mahyar Najibi, Zheng Xu, John Dickerson, Larry S Davis, and Tom Goldstein · 2020
Cited alongside, same era.
Understanding adversarial examples from the mutual influence of images and perturbations
Chaoning Zhang, Philipp Benz, Tooba Imtiaz, and In So Kweon · 2020
Cited alongside, same era.
Unsupervised multi-source domain adaptation without access to source data
Sk Miraj Ahmed, Dripta S Raychaudhuri, Sujoy Paul, Samet Oymak, and Amit K Roy-Chowdhury · 2021
Cited alongside, same era.
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Exploiting the intrinsic neighborhood structure for source-free domain adaptation
Shiqi Yang, Joost van de Weijer, Luis Herranz, Shangling Jui, et al · 2021
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Domain adaptive semantic segmentation without source data
Fuming You, Jingjing Li, Lei Zhu, Zhi Chen, and Zi Huang · 2021
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Rethinking the backdoor attacks’ triggers: A frequency perspective
Yi Zeng, Won Park, Z Morley Mao, and Ruoxi Jia · 2021
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A survey on universal adversarial attack
Chaoning Zhang, Philipp Benz, Chenguo Lin, Adil Karjauv, Jing Wu, and In So Kweon · 2021
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Unsupervised robust domain adaptation without source data
Peshal Agarwal, Danda Pani Paudel, Jan-Nico Zaech, and Luc Van Gool · 2022
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Proxymix: Proxy-based mixup training with label refinery for source-free domain adaptation
Yuhe Ding, Lijun Sheng, Jian Liang, Aihua Zheng, and Ran He · 2022
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Few-shot backdoor defense using shapley estimation
Jiyang Guan, Zhuozhuo Tu, Ran He, and Dacheng Tao · 2022
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Source-free object detection by learning to overlook domain style
Shuaifeng Li, Mao Ye, Xiatian Zhu, Lihua Zhou, and Lin Xiong · 2022
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Backdoor learning: A survey
Yiming Li, Yong Jiang, Zhifeng Li, and Shu-Tao Xia · 2022
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Dine: Domain adaptation from single and multiple black-box predictors
Jian Liang, Dapeng Hu, Jiashi Feng, and Ran He · 2022
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Towards interpretable defense against adversarial attacks via causal inference
Min Ren, Yun-Long Wang, and Zhao-Feng He · 2022
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Backdoorbench: A comprehensive benchmark of backdoor learning
Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, Chao Shen, and Hongyuan Zha · 2022
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Attracting and dispersing: A simple approach for source-free domain adaptation
Shiqi Yang, Shangling Jui, Joost van de Weijer, et al · 2022
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Yuhe Ding, Jian Liang, Bo Jiang, Aihua Zheng, and Ran He · 2023
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A comprehensive survey on test-time adaptation under distribution shifts
Jian Liang, Ran He, and Tieniu Tan · 2023
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Red alarm for pre-trained models: Universal vulnerability to neuron-level backdoor attacks
Zhengyan Zhang, Guangxuan Xiao, Yongwei Li, Tian Lv, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Xin Jiang, and Maosong Sun · 2023
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