{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T07:28:11Z","timestamp":1780471691394,"version":"3.54.1"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"ETRA","license":[{"start":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T00:00:00Z","timestamp":1652400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:p>The study of human gaze behavior in natural contexts requires algorithms for gaze estimation that are robust to a wide range of imaging conditions. However, algorithms often fail to identify features such as the iris and pupil centroid in the presence of reflective artifacts and occlusions. Previous work has shown that convolutional networks excel at extracting gaze features despite the presence of such artifacts. However, these networks often perform poorly on data unseen during training. This work follows the intuition that jointly training a convolutional network with multiple datasets learns a generalized representation of eye parts. We compare the performance of a single model trained with multiple datasets against a pool of models trained on individual datasets. Results indicate that models tested on datasets in which eye images exhibit higher appearance variability benefit from multiset training. In contrast, dataset-specific models generalize better onto eye images with lower appearance variability.<\/jats:p>","DOI":"10.1145\/3530880","type":"journal-article","created":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T22:17:43Z","timestamp":1652480263000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["EllSeg-Gen, towards Domain Generalization for Head-Mounted Eyetracking"],"prefix":"10.1145","volume":"6","author":[{"given":"Rakshit S.","family":"Kothari","sequence":"first","affiliation":[{"name":"Rochester Institute of Technology, Rochester, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reynold J.","family":"Bailey","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, Rochester, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Kanan","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, Rochester, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeff B.","family":"Pelz","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, Rochester, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriel J.","family":"Diaz","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, Rochester, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Metareg: Towards domain generalization using meta-regularization. Advances in Neural Information Processing Systems 2018-Decem, NeurIPS","author":"Balaji Yogesh","year":"2018","unstructured":"1 ] Yogesh Balaji , Swami Sankaranarayanan , and Rama Chellappa . 2018 . Metareg: Towards domain generalization using meta-regularization. Advances in Neural Information Processing Systems 2018-Decem, NeurIPS (2018), 998--1008. 1] Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa. 2018. Metareg: Towards domain generalization using meta-regularization. Advances in Neural Information Processing Systems 2018-Decem, NeurIPS (2018), 998--1008."},{"key":"e_1_2_2_2_1","volume-title":"A theory of learning from di#erent domains. Machine learning 79, 1--2","author":"Ben-David Shai","year":"2010","unstructured":"Shai Ben-David , John Blitzer , Koby Crammer , Alex Kulesza , Fernando Pereira , and Jennifer Wortman Vaughan . 2010. A theory of learning from di#erent domains. Machine learning 79, 1--2 ( 2010 ), 151--175. Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A theory of learning from di#erent domains. Machine learning 79, 1--2 (2010), 151--175."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl242"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00568"},{"key":"e_1_2_2_5_1","volume-title":"Eye Tracking Research and Applications Symposium (ETRA)","author":"Dierkes Kai","year":"2018","unstructured":"Kai Dierkes , Moritz Kassner , and Andreas Bulling . 2018 . A novel approach to single camera, glint-free 3D eye model \"tting including corneal refraction . Eye Tracking Research and Applications Symposium (ETRA) June (2018). https:\/\/doi.org\/10.1145\/3204493.3204525 10.1145\/3204493.3204525 Kai Dierkes, Moritz Kassner, and Andreas Bulling. 2018. A novel approach to single camera, glint-free 3D eye model \"tting including corneal refraction. Eye Tracking Research and Applications Symposium (ETRA) June (2018). https:\/\/doi.org\/10.1145\/3204493.3204525"},{"key":"e_1_2_2_6_1","volume-title":"A fast approach to refraction-aware eye-model \"tting and gaze prediction. June","author":"Dierkes Kai","year":"2019","unstructured":"Kai Dierkes , Moritz Kassner , and Andreas Bulling . 2019. A fast approach to refraction-aware eye-model \"tting and gaze prediction. June ( 2019 ), 1--9. https:\/\/doi.org\/10.1145\/3314111.3319819 10.1145\/3314111.3319819 Kai Dierkes, Moritz Kassner, and Andreas Bulling. 2019. A fast approach to refraction-aware eye-model \"tting and gaze prediction. June (2019), 1--9. https:\/\/doi.org\/10.1145\/3314111.3319819"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3002417"},{"key":"#cr-split#-e_1_2_2_8_1.1","doi-asserted-by":"crossref","unstructured":"Shaharam Eivazi Thiago Santini Alireza Keshavarzi Thomas K\u00fcbler and Andrea Mazzei. 2019. Improving real-time CNN-based pupil detection through domain-speci\"c data augmentation. (2019) 1--6. https:\/\/doi.org\/10.1145\/3314111. 3319914 10.1145\/3314111","DOI":"10.1145\/3314111"},{"key":"#cr-split#-e_1_2_2_8_1.2","doi-asserted-by":"crossref","unstructured":"Shaharam Eivazi Thiago Santini Alireza Keshavarzi Thomas K\u00fcbler and Andrea Mazzei. 2019. Improving real-time CNN-based pupil detection through domain-speci\"c data augmentation. (2019) 1--6. https:\/\/doi.org\/10.1145\/3314111. 3319914","DOI":"10.1145\/3314111.3319914"},{"key":"e_1_2_2_9_1","volume-title":"International Conference on Machine Learning. PMLR, 1126--1135","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn , Pieter Abbeel , and Sergey Levine . 2017 . Model-agnostic meta-learning for fast adaptation of deep networks . In International Conference on Machine Learning. PMLR, 1126--1135 . Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning. PMLR, 1126--1135."},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23192-1_4"},{"key":"e_1_2_2_11_1","volume-title":"Lecture Notes in Computer Science","author":"Fuhl Wolfgang","unstructured":"Wolfgang Fuhl , Wolfgang Rosenstiel , and Enkelejda Kasneci . 2019. 500,000 Images Closer to Eyelid and Pupil Segmentation . Lecture Notes in Computer Science , Vol. 11678 LNCS. Springer International Publishing , Cham . 336--347 pages. https:\/\/doi.org\/10.1007\/978--3-030--29888--3{_}27 10.1007\/978--3-030--29888--3 Wolfgang Fuhl, Wolfgang Rosenstiel, and Enkelejda Kasneci. 2019. 500,000 Images Closer to Eyelid and Pupil Segmentation. Lecture Notes in Computer Science, Vol. 11678 LNCS. Springer International Publishing, Cham. 336--347 pages. https:\/\/doi.org\/10.1007\/978--3-030--29888--3{_}27"},{"key":"e_1_2_2_12_1","unstructured":"Wolfgang Fuhl Thiago Santini Gjergji Kasneci Wolfgang Rosenstiel and Enkelejda Kasneci. 2017. PupilNet v2.0: Convolutional Neural Networks for CPU based real time Robust Pupil Detection. (2017). http:\/\/arxiv.org\/abs\/1711.00112  Wolfgang Fuhl Thiago Santini Gjergji Kasneci Wolfgang Rosenstiel and Enkelejda Kasneci. 2017. PupilNet v2.0: Convolutional Neural Networks for CPU based real time Robust Pupil Detection. (2017). http:\/\/arxiv.org\/abs\/1711.00112"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2857491.2857505"},{"key":"e_1_2_2_14_1","volume-title":"International conference on machine learning. PMLR, 1180--1189","author":"Ganin Yaroslav","year":"2015","unstructured":"Yaroslav Ganin and Victor Lempitsky . 2015 . Unsupervised domain adaptation by backpropagation . In International conference on machine learning. PMLR, 1180--1189 . Yaroslav Ganin and Victor Lempitsky. 2015. Unsupervised domain adaptation by backpropagation. In International conference on machine learning. PMLR, 1180--1189."},{"key":"e_1_2_2_15_1","volume-title":"Talathi","author":"Garbin Stephan J.","year":"2019","unstructured":"Stephan J. Garbin , Yiru Shen , Immo Schuetz , Robert Cavin , Gregory Hughes , and Sachin S . Talathi . 2019 . OpenEDS: Open Eye Dataset . (2019). http:\/\/arxiv.org\/abs\/1905.03702 Stephan J. Garbin, Yiru Shen, Immo Schuetz, Robert Cavin, Gregory Hughes, and Sachin S. Talathi. 2019. OpenEDS: Open Eye Dataset. (2019). http:\/\/arxiv.org\/abs\/1905.03702"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.293"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450341.3458494"},{"key":"e_1_2_2_18_1","volume-title":"Generative adversarial nets. Advances in neural information processing systems 27","author":"Goodfellow Ian","year":"2014","unstructured":"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 ). 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)."},{"key":"e_1_2_2_19_1","unstructured":"Ishaan Gulrajani and David Lopez-Paz. 2020. In Search of Lost Domain Generalization. (2020). arXiv:2007.01434 http:\/\/arxiv.org\/abs\/2007.01434  Ishaan Gulrajani and David Lopez-Paz. 2020. In Search of Lost Domain Generalization. (2020). arXiv:2007.01434 http:\/\/arxiv.org\/abs\/2007.01434"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01418-6_10"},{"key":"e_1_2_2_21_1","volume-title":"Episodic Training for Domain Generalization Using Latent Domains. In International Conference on Cognitive Systems and Signal Processing. Springer, 85--93","author":"Huang Bincheng","year":"2020","unstructured":"Bincheng Huang , Si Chen , Fan Zhou , Cheng Zhang , and Feng Zhang . 2020 . Episodic Training for Domain Generalization Using Latent Domains. In International Conference on Cognitive Systems and Signal Processing. Springer, 85--93 . Bincheng Huang, Si Chen, Fan Zhou, Cheng Zhang, and Feng Zhang. 2020. Episodic Training for Domain Generalization Using Latent Domains. In International Conference on Cognitive Systems and Signal Processing. Springer, 85--93."},{"key":"e_1_2_2_22_1","volume-title":"International conference on machine learning. PMLR, 448--456","author":"Christian Szegedy Sergey","year":"2015","unstructured":"Sergey Io#e and Christian Szegedy . 2015 . Batch normalization: Accelerating deep network training by reducing internal covariate shift . In International conference on machine learning. PMLR, 448--456 . Sergey Io#e and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning. PMLR, 448--456."},{"key":"e_1_2_2_23_1","volume-title":"The distribution of the !ora in the alpine zone. 1. New phytologist 11, 2","author":"Jaccard Paul","year":"1912","unstructured":"Paul Jaccard . 1912. The distribution of the !ora in the alpine zone. 1. New phytologist 11, 2 ( 1912 ), 37--50. Paul Jaccard. 1912. The distribution of the !ora in the alpine zone. 1. New phytologist 11, 2 (1912), 37--50."},{"key":"e_1_2_2_24_1","volume-title":"The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. IEEE Computer Society Conference on Computer Proc. ACM Hum.-Comput. Interact.","volume":"6","author":"Jegou Simon","year":"2017","unstructured":"Simon Jegou , Michal Drozdzal , David Vazquez , Adriana Romero , and Yoshua Bengio . 2017 . The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. IEEE Computer Society Conference on Computer Proc. ACM Hum.-Comput. Interact. , Vol. 6 , No. ETRA, Article 139. Publication date : May 2022. 139:16 Rakshit S. Kothari et al. Vision and Pattern Recognition Workshops 2017-July (2017), 1175--1183. https:\/\/doi.org\/10.1109\/CVPRW.2017.156 10.1109\/CVPRW.2017.156 Simon Jegou, Michal Drozdzal, David Vazquez, Adriana Romero, and Yoshua Bengio. 2017. The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. IEEE Computer Society Conference on Computer Proc. ACM Hum.-Comput. Interact., Vol. 6, No. ETRA, Article 139. Publication date: May 2022. 139:16 Rakshit S. Kothari et al. Vision and Pattern Recognition Workshops 2017-July (2017), 1175--1183. https:\/\/doi.org\/10.1109\/CVPRW.2017.156"},{"key":"e_1_2_2_25_1","volume-title":"Chi-Hung Weng, Abner Ayala-Acevedo, Raphael Meudec, Matias Laporte, et al.","author":"Jung Alexander B.","year":"2020","unstructured":"Alexander B. Jung , Kentaro Wada , Jon Crall , Satoshi Tanaka , Jake Graving , Christoph Reinders , Sarthak Yadav , Joy Banerjee , G\u00e1bor Vecsei , Adam Kraft , Zheng Rui , Jirka Borovec , Christian Vallentin , Semen Zhydenko , Kilian Pfei#er, Ben Cook , Ismael Fern\u00e1ndez , Fran\u00e7ois-Michel De Rainville , Chi-Hung Weng, Abner Ayala-Acevedo, Raphael Meudec, Matias Laporte, et al. 2020 . imgaug. https:\/\/github.com\/aleju\/imgaug. Online ; accessed 01-Feb-2020. Alexander B. Jung, Kentaro Wada, Jon Crall, Satoshi Tanaka, Jake Graving, Christoph Reinders, Sarthak Yadav, Joy Banerjee, G\u00e1bor Vecsei, Adam Kraft, Zheng Rui, Jirka Borovec, Christian Vallentin, Semen Zhydenko, Kilian Pfei#er, Ben Cook, Ismael Fern\u00e1ndez, Fran\u00e7ois-Michel De Rainville, Chi-Hung Weng, Abner Ayala-Acevedo, Raphael Meudec, Matias Laporte, et al. 2020. imgaug. https:\/\/github.com\/aleju\/imgaug. Online; accessed 01-Feb-2020."},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00456"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0087470"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2638728.2641695"},{"key":"e_1_2_2_29_1","volume-title":"What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems 30","author":"Kendall Alex","year":"2017","unstructured":"Alex Kendall and Yarin Gal . 2017. What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems 30 ( 2017 ). Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems 30 (2017)."},{"key":"e_1_2_2_30_1","first-page":"158","article-title":"Undoing the damage of dataset bias. Lecture Notes in Computer Science (including subseries Lecture Notes in Arti!cial Intelligence and Lecture Notes in Bioinformatics) 7572 LNCS","volume":"1","author":"Khosla Aditya","year":"2012","unstructured":"Aditya Khosla , Tinghui Zhou , Tomasz Malisiewicz , Alexei A. Efros , and Antonio Torralba . 2012 . Undoing the damage of dataset bias. Lecture Notes in Computer Science (including subseries Lecture Notes in Arti!cial Intelligence and Lecture Notes in Bioinformatics) 7572 LNCS , PART 1 (2012), 158 -- 171 . https:\/\/doi.org\/10.1007\/978--3--642--33718--5_12 10.1007\/978--3--642--33718--5_12 Aditya Khosla, Tinghui Zhou, Tomasz Malisiewicz, Alexei A. Efros, and Antonio Torralba. 2012. Undoing the damage of dataset bias. Lecture Notes in Computer Science (including subseries Lecture Notes in Arti!cial Intelligence and Lecture Notes in Bioinformatics) 7572 LNCS, PART 1 (2012), 158--171. https:\/\/doi.org\/10.1007\/978--3--642--33718--5_12","journal-title":"PART"},{"key":"e_1_2_2_31_1","volume-title":"Adam: A Method for Stochastic Optimization. AIP Conference Proceedings 1631","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014 . Adam: A Method for Stochastic Optimization. AIP Conference Proceedings 1631 , 2 (12 2014 ), 58--62. https:\/\/doi.org\/10.1016\/j.jneumeth.2005.04.009 10.1016\/j.jneumeth.2005.04.009 Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. AIP Conference Proceedings 1631, 2 (12 2014), 58--62. https:\/\/doi.org\/10.1016\/j.jneumeth.2005.04.009"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2021.3067765"},{"key":"e_1_2_2_33_1","volume-title":"An introduction to domain adaptation and transfer learning. arXiv preprint arXiv:1812.11806","author":"Kouw Wouter M","year":"2018","unstructured":"Wouter M Kouw and Marco Loog . 2018. An introduction to domain adaptation and transfer learning. arXiv preprint arXiv:1812.11806 ( 2018 ). Wouter M Kouw and Marco Loog. 2018. An introduction to domain adaptation and transfer learning. arXiv preprint arXiv:1812.11806 (2018)."},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2945942"},{"key":"e_1_2_2_35_1","volume-title":"Proceedings of the AAAI Conference on Arti!cial Intelligence","volume":"32","author":"Li Da","year":"2018","unstructured":"Da Li , Yongxin Yang , Yi-Zhe Song , and Timothy Hospedales . 2018 . Learning to generalize: Meta-learning for domain generalization . In Proceedings of the AAAI Conference on Arti!cial Intelligence , Vol. 32 . Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales. 2018. Learning to generalize: Meta-learning for domain generalization. In Proceedings of the AAAI Conference on Arti!cial Intelligence, Vol. 32."},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"e_1_2_2_38_1","volume-title":"Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083","author":"Madry Aleksander","year":"2017","unstructured":"Aleksander Madry , Aleksandar Makelov , Ludwig Schmidt , Dimitris Tsipras , and Adrian Vladu . 2017. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 ( 2017 ). Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)."},{"key":"e_1_2_2_39_1","volume-title":"30th International Conference on Machine Learning, ICML 2013 PART 1","author":"Muandet Krikamol","year":"2013","unstructured":"Krikamol Muandet , David Balduzzi , and Bernhard Sch\u00f6lkopf . 2013 . Domain generalization via invariant feature representation . 30th International Conference on Machine Learning, ICML 2013 PART 1 (2013), 10--18. arXiv:1301.2115 Krikamol Muandet, David Balduzzi, and Bernhard Sch\u00f6lkopf. 2013. Domain generalization via invariant feature representation. 30th International Conference on Machine Learning, ICML 2013 PART 1 (2013), 10--18. arXiv:1301.2115"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3385955.3407935"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2019.00083"},{"key":"e_1_2_2_42_1","volume-title":"Deep Pictorial Gaze Estimation","author":"Park Seonwook","unstructured":"Seonwook Park , Adrian Spurr , and Otmar Hilliges . 2018. Deep Pictorial Gaze Estimation . Vol. 11217 LNCS. 741--757. https:\/\/doi.org\/10.1007\/978--3-030-01261--8{_}44 10.1007\/978--3-030-01261--8 Seonwook Park, Adrian Spurr, and Otmar Hilliges. 2018. Deep Pictorial Gaze Estimation. Vol. 11217 LNCS. 741--757. https:\/\/doi.org\/10.1007\/978--3-030-01261--8{_}44"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3204493.3204545"},{"key":"e_1_2_2_44_1","doi-asserted-by":"crossref","unstructured":"Jean Ponce Tamara L Berg Mark Everingham David A Forsyth Martial Hebert Svetlana Lazebnik Marcin Marszalek Cordelia Schmid Bryan C Russell Antonio Torralba etal 2006. Dataset issues in object recognition. In Toward category-level object recognition. Springer 29--48.  Jean Ponce Tamara L Berg Mark Everingham David A Forsyth Martial Hebert Svetlana Lazebnik Marcin Marszalek Cordelia Schmid Bryan C Russell Antonio Torralba et al. 2006. Dataset issues in object recognition. In Toward category-level object recognition. Springer 29--48.","DOI":"10.1007\/11957959_2"},{"key":"e_1_2_2_45_1","volume-title":"Fanny Yang, John Duchi, and Percy Liang.","author":"Raghunathan Aditi","year":"2020","unstructured":"Aditi Raghunathan , Sang Michael Xie , Fanny Yang, John Duchi, and Percy Liang. 2020 . Understanding and mitigating the tradeo# between robustness and accuracy. arXiv preprint arXiv:2002.10716 (2020). Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, and Percy Liang. 2020. Understanding and mitigating the tradeo# between robustness and accuracy. arXiv preprint arXiv:2002.10716 (2020)."},{"key":"e_1_2_2_46_1","volume-title":"U-net: Convolutional networks for biomedical image segmentation. 9351","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger , Philipp Fischer , and Thomas Brox . 2015 . U-net: Convolutional networks for biomedical image segmentation. 9351 (2015), 234--241. https:\/\/doi.org\/10.1007\/978--3--319--24574--4{_}28 10.1007\/978--3--319--24574--4 Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. 9351 (2015), 234--241. https:\/\/doi.org\/10.1007\/978--3--319--24574--4{_}28"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3314111.3319835"},{"key":"e_1_2_2_48_1","volume-title":"Proceedings of the 32nd international conference on neural information processing systems. 2488--2498","author":"Santurkar Shibani","year":"2018","unstructured":"Shibani Santurkar , Dimitris Tsipras , Andrew Ilyas , and Aleksander Madry . 2018 . How does batch normalization help optimization? . In Proceedings of the 32nd international conference on neural information processing systems. 2488--2498 . Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry. 2018. How does batch normalization help optimization?. In Proceedings of the 32nd international conference on neural information processing systems. 2488--2498."},{"key":"e_1_2_2_49_1","volume-title":"Domain Adaptation for Eye Segmentation. In European Conference on Computer Vision. Springer, 555--569","author":"Shen Yiru","year":"2020","unstructured":"Yiru Shen , Oleg Komogortsev , and Sachin S Talathi . 2020 . Domain Adaptation for Eye Segmentation. In European Conference on Computer Vision. Springer, 555--569 . Yiru Shen, Oleg Komogortsev, and Sachin S Talathi. 2020. Domain Adaptation for Eye Segmentation. In European Conference on Computer Vision. Springer, 555--569."},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_2_2_51_1","volume-title":"IBM Report (November","author":"Tanimoto TT","year":"1958","unstructured":"TT Tanimoto . 1968. An elementary mathematical theory of classi\"cation and prediction , IBM Report (November , 1958 ), cited in: G. Salton, Automatic Information Organization and Retrieval . TT Tanimoto. 1968. An elementary mathematical theory of classi\"cation and prediction, IBM Report (November, 1958), cited in: G. Salton, Automatic Information Organization and Retrieval."},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1987.10478458"},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2857491.2857520"},{"key":"e_1_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"e_1_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"e_1_2_2_56_1","volume-title":"Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474","author":"Tzeng Eric","year":"2014","unstructured":"Eric Tzeng , Judy Ho#man, Ning Zhang , Kate Saenko , and Trevor Darrell . 2014. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474 ( 2014 ). Eric Tzeng, Judy Ho#man, Ning Zhang, Kate Saenko, and Trevor Darrell. 2014. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014)."},{"key":"e_1_2_2_57_1","volume-title":"Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022","author":"Ulyanov Dmitry","year":"2016","unstructured":"Dmitry Ulyanov , Andrea Vedaldi , and Victor Lempitsky . 2016. Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 ( 2016 ). Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2016. Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)."},{"key":"e_1_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1198\/10618600152418584"},{"key":"e_1_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.788640"},{"key":"e_1_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.3233\/ICA-180584"},{"key":"e_1_2_2_61_1","first-page":"1","article-title":"The e#ectiveness of data augmentation in image classi","volume":"11","author":"Wang Jason","year":"2017","unstructured":"Jason Wang , Luis Perez , 2017 . The e#ectiveness of data augmentation in image classi \"cation using deep learning. Convolutional Neural Networks Vis. Recognit 11 (2017), 1 -- 8 . Jason Wang, Luis Perez, et al. 2017. The e#ectiveness of data augmentation in image classi\"cation using deep learning. Convolutional Neural Networks Vis. Recognit 11 (2017), 1--8.","journal-title":"Convolutional Neural Networks Vis. Recognit"},{"key":"e_1_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/2578153.2578185"},{"key":"e_1_2_2_63_1","volume-title":"A closer look at accuracy vs. robustness. Advances in neural information processing systems 33","author":"Yang Yao-Yuan","year":"2020","unstructured":"Yao-Yuan Yang , Cyrus Rashtchian , Hongyang Zhang , Russ R Salakhutdinov , and Kamalika Chaudhuri . 2020. A closer look at accuracy vs. robustness. Advances in neural information processing systems 33 ( 2020 ), 8588--8601. Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Russ R Salakhutdinov, and Kamalika Chaudhuri. 2020. A closer look at accuracy vs. robustness. Advances in neural information processing systems 33 (2020), 8588--8601."},{"key":"e_1_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2019.05.016"},{"key":"e_1_2_2_65_1","volume-title":"International Conference on Machine Learning. PMLR, 7472-- 7482","author":"Zhang Hongyang","year":"2019","unstructured":"Hongyang Zhang , Yaodong Yu , Jiantao Jiao , Eric Xing , Laurent El Ghaoui , and Michael Jordan . 2019 . Theoretically principled trade-o# between robustness and accuracy . In International Conference on Machine Learning. PMLR, 7472-- 7482 . Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. 2019. Theoretically principled trade-o# between robustness and accuracy. In International Conference on Machine Learning. PMLR, 7472-- 7482."}],"container-title":["Proceedings of the ACM on Human-Computer Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3530880","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3530880","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:26Z","timestamp":1750183766000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3530880"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,13]]},"references-count":66,"journal-issue":{"issue":"ETRA","published-print":{"date-parts":[[2022,5,13]]}},"alternative-id":["10.1145\/3530880"],"URL":"https:\/\/doi.org\/10.1145\/3530880","relation":{},"ISSN":["2573-0142"],"issn-type":[{"value":"2573-0142","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,13]]},"assertion":[{"value":"2022-05-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}