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Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in neural information processing systems 27 (2014), 2672--2680."},{"key":"e_1_3_2_2_15_1","volume-title":"3rd International Conference on Learning Representations, ICLR","author":"Goodfellow Ian J.","year":"2015","unstructured":"Ian J. Goodfellow , Jonathon Shlens , and Christian Szegedy . 2015. Explaining and Harnessing Adversarial Examples . In 3rd International Conference on Learning Representations, ICLR 2015 , San Diego, CA , USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds .). Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015. Explaining and Harnessing Adversarial Examples. 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Mingsheng Long Zhangjie Cao Jianmin Wang and Michael I Jordan. 2018. Conditional adversarial domain adaptation. In Advances in neural information processing systems. 1640--1650."},{"key":"e_1_3_2_2_22_1","volume-title":"6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net.","author":"Madry Aleksander","year":"2018","unstructured":"Aleksander Madry , Aleksandar Makelov , Ludwig Schmidt , Dimitris Tsipras , and Adrian Vladu . 2018 . Towards Deep Learning Models Resistant to Adversarial Attacks . In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. 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In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6--14, 2021, virtual, Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 3571--3583."},{"key":"e_1_3_2_2_24_1","volume-title":"Virtual adversarial training: a regularization method for supervised and semi-supervised learning","author":"Miyato Takeru","year":"2018","unstructured":"Takeru Miyato , Shin-ichi Maeda, Masanori Koyama , and Shin Ishii . 2018. Virtual adversarial training: a regularization method for supervised and semi-supervised learning . IEEE transactions on pattern analysis and machine intelligence 41, 8 ( 2018 ), 1979--1993. Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. 2018. Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE transactions on pattern analysis and machine intelligence 41, 8 (2018), 1979--1993."},{"key":"e_1_3_2_2_25_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"32","author":"Pei Zhongyi","year":"2018","unstructured":"Zhongyi Pei , Zhangjie Cao , Mingsheng Long , and Jianmin Wang . 2018 . Multiadversarial domain adaptation . In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 32 . Zhongyi Pei, Zhangjie Cao, Mingsheng Long, and Jianmin Wang. 2018. Multiadversarial domain adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32."},{"key":"e_1_3_2_2_26_1","volume-title":"VisDA: The Visual Domain Adaptation Challenge. CoRR abs\/1710.06924","author":"Peng Xingchao","year":"2017","unstructured":"Xingchao Peng , Ben Usman , Neela Kaushik , Judy Hoffman , Dequan Wang , and Kate Saenko . 2017. VisDA: The Visual Domain Adaptation Challenge. CoRR abs\/1710.06924 ( 2017 ). Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko. 2017. VisDA: The Visual Domain Adaptation Challenge. CoRR abs\/1710.06924 (2017)."},{"key":"e_1_3_2_2_27_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18","volume":"8104","author":"Rice Leslie","year":"2020","unstructured":"Leslie Rice , EricWong, and J. Zico Kolter . 2020. Overfitting in adversarially robust deep learning . In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18 July 2020 , Virtual Event (Proceedings of Machine Learning Research , Vol. 119). PMLR, 8093-- 8104 . Leslie Rice, EricWong, and J. Zico Kolter. 2020. Overfitting in adversarially robust deep learning. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18 July 2020, Virtual Event (Proceedings of Machine Learning Research, Vol. 119). 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In Computer Vision - ECCV 2010, 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5--11, 2010, Proceedings, Part IV (Lecture Notes in Computer Science, Vol. 6314), Kostas Daniilidis, Petros Maragos, and Nikos Paragios (Eds.). Springer, 213--226."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"e_1_3_2_2_30_1","volume-title":"Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Salman Hadi","year":"2020","unstructured":"Hadi Salman , Andrew Ilyas , Logan Engstrom , Ashish Kapoor , and Aleksander Madry . 2020 . Do Adversarially Robust ImageNet Models Transfer Better? . In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020 , NeurIPS 2020, December 6--12, 2020, virtual, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry. 2020. Do Adversarially Robust ImageNet Models Transfer Better?. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.)."},{"key":"e_1_3_2_2_31_1","volume-title":"Adversarially Robust Generalization Requires More Data. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018","author":"Schmidt Ludwig","year":"2018","unstructured":"Ludwig Schmidt , Shibani Santurkar , Dimitris Tsipras , Kunal Talwar , and Aleksander Madry . 2018 . Adversarially Robust Generalization Requires More Data. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018 , NeurIPS 2018, December 3--8, 2018, Montr\u00e9al, Canada, Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 5019--5031. Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry. 2018. Adversarially Robust Generalization Requires More Data. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3--8, 2018, Montr\u00e9al, Canada, Samy Bengio, Hanna M. 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In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14--16, 2014, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6054"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.463"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"e_1_3_2_2_39_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of machine learning research 9 , 11 (2008). Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_40_1","volume-title":"Deep Hashing Network for Unsupervised Domain Adaptation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017","author":"Venkateswara Hemanth","year":"2017","unstructured":"Hemanth Venkateswara , Jose Eusebio , Shayok Chakraborty , and Sethuraman Panchanathan . 2017 . Deep Hashing Network for Unsupervised Domain Adaptation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 , Honolulu, HI, USA, July 21--26 , 2017. IEEE Computer Society, 5385--5394. Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan. 2017. Deep Hashing Network for Unsupervised Domain Adaptation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21--26, 2017. IEEE Computer Society, 5385--5394."},{"key":"e_1_3_2_2_41_1","volume-title":"Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018","author":"Volpi Riccardo","year":"2018","unstructured":"Riccardo Volpi , Hongseok Namkoong , Ozan Sener , John C. Duchi , Vittorio Murino , and Silvio Savarese . 2018 . Generalizing to Unseen Domains via Adversarial Data Augmentation . In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018 , NeurIPS 2018, December 3--8, 2018, Montr\u00e9al, Canada, Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 5339--5349. Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, and Silvio Savarese. 2018. Generalizing to Unseen Domains via Adversarial Data Augmentation. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3--8, 2018, Montr\u00e9al, Canada, Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 5339--5349."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3093468"},{"key":"e_1_3_2_2_43_1","volume-title":"Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017","author":"Yan Hongliang","year":"2017","unstructured":"Hongliang Yan , Yukang Ding , Peihua Li , Qilong Wang , Yong Xu , and Wangmeng Zuo . 2017 . Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 , Honolulu, HI, USA, July 21--26 , 2017. IEEE Computer Society, 945--954. Hongliang Yan, Yukang Ding, Peihua Li, Qilong Wang, Yong Xu, and Wangmeng Zuo. 2017. Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21--26, 2017. IEEE Computer Society, 945--954."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00906"},{"key":"e_1_3_2_2_45_1","volume-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems","author":"Zhang Haichao","year":"2019","unstructured":"Haichao Zhang and Jianyu Wang . 2019. Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training . In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 , NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alch\u00e9-Buc, Emily B. Fox, and Roman Garnett (Eds .). 1829--1839. Haichao Zhang and Jianyu Wang. 2019. Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alch\u00e9-Buc, Emily B. Fox, and Roman Garnett (Eds.). 1829--1839."},{"key":"e_1_3_2_2_46_1","volume-title":"Towards Better Robust Generalization with Shift Consistency Regularization. In International Conference on Machine Learning. PMLR, 12524--12534","author":"Zhang Shufei","year":"2021","unstructured":"Shufei Zhang , Zhuang Qian , Kaizhu Huang , Qiufeng Wang , Rui Zhang , and Xinping Yi . 2021 . Towards Better Robust Generalization with Shift Consistency Regularization. In International Conference on Machine Learning. PMLR, 12524--12534 . Shufei Zhang, Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Rui Zhang, and Xinping Yi. 2021. Towards Better Robust Generalization with Shift Consistency Regularization. In International Conference on Machine Learning. PMLR, 12524--12534."},{"key":"e_1_3_2_2_47_1","volume-title":"Bridging Theory and Algorithm for Domain Adaptation. In International Conference on Machine Learning. 7404--7413","author":"Zhang Yuchen","year":"2019","unstructured":"Yuchen Zhang , Tianle Liu , Mingsheng Long , and Michael Jordan . 2019 . Bridging Theory and Algorithm for Domain Adaptation. In International Conference on Machine Learning. 7404--7413 . Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael Jordan. 2019. Bridging Theory and Algorithm for Domain Adaptation. In International Conference on Machine Learning. 7404--7413."}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","location":"Lisboa Portugal","acronym":"MM '22","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548323","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3548323","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:43Z","timestamp":1750186843000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548323"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":47,"alternative-id":["10.1145\/3503161.3548323","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3548323","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}