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Surv."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>In the evolution of agriculture to its next stage, Agriculture 5.0, artificial intelligence will play a central role. Controlled-environment agriculture, or CEA, is a special form of urban and suburban agricultural practice that offers numerous economic, environmental, and social benefits, including shorter transportation routes to population centers, reduced environmental impact, and increased productivity. Due to its ability to control environmental factors, CEA couples well with computer vision (CV) in the adoption of real-time monitoring of the plant conditions and autonomous cultivation and harvesting. The objective of this article is to familiarize CV researchers with agricultural applications and agricultural practitioners with the solutions offered by CV. We identify five major CV applications in CEA, analyze their requirements and motivation, and survey the state-of-the-art as reflected in 68 technical papers using deep learning methods. In addition, we discuss five key subareas of computer vision and how they related to these CEA problems, as well as 14 vision-based CEA datasets. We hope the survey will help researchers quickly gain a bird\u2019s-eye view of the striving research area and will spark inspiration for new research and development.<\/jats:p>","DOI":"10.1145\/3626186","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T14:26:53Z","timestamp":1696343213000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["A Survey of Computer Vision Technologies in Urban and Controlled-environment Agriculture"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4151-6682","authenticated-orcid":false,"given":"Jiayun","family":"Luo","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6230-2376","authenticated-orcid":false,"given":"Boyang","family":"Li","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9911-2069","authenticated-orcid":false,"given":"Cyril","family":"Leung","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore and China-Singapore International Joint Research Institute, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Singapore Food Agency. 2023. 30 by 30. 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Retrieved 28 July 2022 from https:\/\/www.hortidaily.com\/article\/9212847\/tomatoes-and-cucumbers-in-a-vertical-farm-without-daylight\/"},{"key":"e_1_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Moloud Abdar Farhad Pourpanah Sadiq Hussain Dana Rezazadegan Li Liu Mohammad Ghavamzadeh Paul Fieguth Xiaochun Cao Abbas Khosravi U. Rajendra Acharya Vladimir Makarenkov and Saeid Nahavandi. 2021. A review of uncertainty quantification in deep learning: Techniques applications and challenges. Information Fusion 76 (2021) 243\u2013297.","DOI":"10.1016\/j.inffus.2021.05.008"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.429489"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2020.571299"},{"key":"e_1_3_1_15_2","unstructured":"I. Ahern A. Noack L. Guzman-Nateras D. Dou B. Li and J. Huan. 2019. 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Can Rationalization Improve Robustness? arXiv preprint arXiv:2204.11790 (2022).","journal-title":"arXiv preprint arXiv:2204.11790"},{"key":"e_1_3_1_61_2","article-title":"Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection","author":"Chen Hanjie","year":"2020","unstructured":"Hanjie Chen, Guangtao Zheng, and Yangfeng Ji. 2020. Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection. arXiv preprint arXiv:2004.02015 (2020).","journal-title":"arXiv preprint arXiv:2004.02015"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00511"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32239-7_50"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_1_66_2","article-title":"A Simple Framework for Contrastive Learning of Visual Representations","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. arXiv 2002.05709 (2020).","journal-title":"arXiv 2002.05709"},{"key":"e_1_3_1_67_2","article-title":"Pix2seq: ALanguage Modeling Framework for Object Detection","author":"Chen Ting","year":"2021","unstructured":"Ting Chen, Saurabh Saxena, Lala Li, David J. Fleet, and Geoffrey Hinton. 2021. Pix2seq: ALanguage Modeling Framework for Object Detection. arXiv preprint arXiv:2109.10852 (2021).","journal-title":"arXiv preprint arXiv:2109.10852"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16871"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298969"},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01249"},{"key":"e_1_3_1_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01613"},{"key":"e_1_3_1_72_2","first-page":"1931","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Cho Jaemin","year":"2021","unstructured":"Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal. 2021. Unifying vision-and-language tasks via text generation. In Proceedings of the International Conference on Machine Learning. PMLR, 1931\u20131942."},{"key":"e_1_3_1_73_2","article-title":"Twins: Revisiting the Design of Spatial Attention in Vision Transformers","author":"Chu Xiangxiang","year":"2021","unstructured":"Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, and Chunhua Shen. 2021. Twins: Revisiting the Design of Spatial Attention in Vision Transformers. arXiv 2104.13840 (2021).","journal-title":"arXiv 2104.13840"},{"key":"e_1_3_1_74_2","volume-title":"Advances in Neural Information Processing Systems","author":"Ciresan Dan","year":"2012","unstructured":"Dan Ciresan, Alessandro Giusti, Luca Gambardella, and J\u00fcrgen Schmidhuber. 2012. Deep neural networks segment neuronal membranes in electron microscopy images. In Advances in Neural Information Processing Systems, F. Pereira, C. J. Burges, L. Bottou, and K. Q. Weinberger (Eds.), Vol. 25. Curran Associates, Inc. Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2012\/file\/459a4ddcb586f24efd9395aa7662bc7c-Paper.pdf"},{"key":"e_1_3_1_75_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11947-016-1767-1"},{"key":"e_1_3_1_76_2","article-title":"RandAugment: Practical automated data augmentation with a reduced search space","author":"Cubuk Ekin D.","year":"2019","unstructured":"Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le. 2019. RandAugment: Practical automated data augmentation with a reduced search space. arXiv 1909.13719 (2019).","journal-title":"arXiv 1909.13719"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_32"},{"key":"e_1_3_1_78_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.89"},{"key":"e_1_3_1_79_2","article-title":"CoAtNet: Marrying Convolution and Attention for All Data Sizes","author":"Dai Zihang","year":"2021","unstructured":"Zihang Dai, Hanxiao Liu, Quoc V. Le, and Mingxing Tan. 2021. CoAtNet: Marrying Convolution and Attention for All Data Sizes. arXiv preprint arXiv:2106.04803 (2021).","journal-title":"arXiv preprint arXiv:2106.04803"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33723-0_14"},{"key":"e_1_3_1_81_2","article-title":"Semantic instance segmentation with a discriminative loss function","author":"Brabandere Bert De","year":"2017","unstructured":"Bert De Brabandere, Davy Neven, and Luc Van Gool. 2017. Semantic instance segmentation with a discriminative loss function. In Proceedings of the Workshop on Deep Learning for Robotic Vision (CVPR\u201917). Retrieved from https:\/\/arxiv.org\/abs\/1708.02551","journal-title":"Proceedings of the Workshop on Deep Learning for Robotic Vision (CVPR\u201917)."},{"key":"e_1_3_1_82_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00482"},{"key":"e_1_3_1_83_2","volume-title":"The Vertical Farm: Feeding the World in the 21st Century","author":"Despommier Dickson","year":"2010","unstructured":"Dickson Despommier. 2010. The Vertical Farm: Feeding the World in the 21st Century. Macmillan."},{"key":"e_1_3_1_84_2","article-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv 1810.04805 (2019).","journal-title":"arXiv 1810.04805"},{"key":"e_1_3_1_85_2","article-title":"Explanations based on the missing: Towards contrastive explanations with pertinent negatives","volume":"31","author":"Dhurandhar Amit","year":"2018","unstructured":"Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. Adv. Neural Inf. Process. Syst. 31 (2018).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_1_86_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2849498"},{"key":"e_1_3_1_87_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2849498"},{"key":"e_1_3_1_88_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00681"},{"key":"e_1_3_1_89_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00912"},{"key":"e_1_3_1_90_2","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture12091350"},{"key":"e_1_3_1_91_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An image is worth 16x16 words: Transformers for image recognition at scale. 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Agricultural Robotics: The Future of Robotic Agriculture. arXiv preprint arXiv:1806.06762 (2018).","journal-title":"arXiv preprint arXiv:1806.06762"},{"issue":"3","key":"e_1_3_1_94_2","first-page":"1","article-title":"Visualizing higher-layer features of a deep network","volume":"1341","author":"Erhan Dumitru","year":"2009","unstructured":"Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2009. Visualizing higher-layer features of a deep network. Univ. Montreal 1341, 3 (2009), 1.","journal-title":"Univ. 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In Proceedings of the 38th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 139), Marina Meila and Tong Zhang (Eds.). PMLR, 3620\u20133629. Retrieved from https:\/\/proceedings.mlr.press\/v139\/garreau21a.html"},{"key":"e_1_3_1_107_2","series-title":"Proceedings of Machine Learning Research","first-page":"1287","volume-title":"Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics","volume":"108","author":"Garreau Damien","year":"2020","unstructured":"Damien Garreau and Ulrike von Luxburg. 2020. Explaining the explainer: A first theoretical analysis of LIME. In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol. 108), Silvia Chiappa and Roberto Calandra (Eds.). PMLR, 1287\u20131296. Retrieved from https:\/\/proceedings.mlr.press\/v108\/garreau20a.html"},{"key":"e_1_3_1_108_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2946369"},{"key":"e_1_3_1_109_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.105165"},{"key":"e_1_3_1_110_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00544"},{"key":"e_1_3_1_111_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"e_1_3_1_112_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00217-012-1844-2"},{"key":"e_1_3_1_113_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodres.2020.109256"},{"key":"e_1_3_1_114_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459211"},{"key":"e_1_3_1_115_2","article-title":"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour","author":"Goyal Priya","year":"2018","unstructured":"Priya Goyal, Piotr Doll\u00e1r, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. 2018. 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JMLR.org, 1321\u20131330."},{"key":"e_1_3_1_119_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.425"},{"key":"e_1_3_1_120_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.425"},{"key":"e_1_3_1_121_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01591"},{"key":"e_1_3_1_122_2","doi-asserted-by":"publisher","DOI":"10.1104\/pp.11.2.445"},{"key":"e_1_3_1_123_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.12.041"},{"key":"e_1_3_1_124_2","first-page":"143","article-title":"Machine vision-based fruit and vegetable disease recognition: A review","author":"Habib Md Tarek","year":"2021","unstructured":"Md Tarek Habib, Md Ariful Islam Arif, Sumaita Binte Shorif, Mohammad Shorif Uddin, and Farruk Ahmed. 2021. Machine vision-based fruit and vegetable disease recognition: A review. Comput. Vis. Mach. Learn. Agric. (2021), 143\u2013157.","journal-title":"Comput. Vis. Mach. Learn. 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Response of greenhouse tomato to different vertical spectra of LED lighting under overhead high pressure sodium and plasma lighting. In Proceedings of the International Symposium on New Technologies and Management for Greenhouses (GreenSys\u201915). 1003\u20131110."},{"key":"e_1_3_1_129_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-4238(98)00217-9"},{"key":"e_1_3_1_130_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10584-0_20"},{"key":"e_1_3_1_131_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR\u201921)","author":"Havasi Marton","year":"2021","unstructured":"Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, and Dustin Tran. 2021. Training independent subnetworks for robust prediction. In Proceedings of the International Conference on Learning Representations (ICLR\u201921)."},{"key":"e_1_3_1_132_2","doi-asserted-by":"publisher","unstructured":"Zeeshan Hayder Xuming He and Mathieu Salzmann. 2016. Boundary-aware Instance Segmentation. DOI:10.48550\/ARXIV.1612.03129","DOI":"10.48550\/ARXIV.1612.03129"},{"key":"e_1_3_1_133_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_3_1_134_2","article-title":"Mask R-CNN","author":"He Kaiming","year":"2018","unstructured":"Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross Girshick. 2018. Mask R-CNN. arXiv 1703.06870 (2018).","journal-title":"arXiv 1703.06870"},{"key":"e_1_3_1_135_2","article-title":"Deep Residual Learning for Image Recognition","author":"He Kaiming","year":"2015","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep Residual Learning for Image Recognition. arXiv 1512.03385 (2015).","journal-title":"arXiv 1512.03385"},{"key":"e_1_3_1_136_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106812"},{"key":"e_1_3_1_137_2","article-title":"What shapes feature representations? Exploring datasets, architectures, and training","author":"Hermann Katherine L.","year":"2020","unstructured":"Katherine L. Hermann and Andrew K. Lampinen. 2020. What shapes feature representations? Exploring datasets, architectures, and training. arXiv2006.12433 (2020).","journal-title":"arXiv2006.12433"},{"key":"e_1_3_1_138_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Cisse Yann N. Dauphin, David Lopez-Paz, Hongyi Zhang, and Moustapha","year":"2018","unstructured":"Yann N. Dauphin, David Lopez-Paz, Hongyi Zhang, and Moustapha Cisse. 2018. Mixup: Beyond empirical risk minimization. In Proceedings of the International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=r1Ddp1-Rb"},{"key":"e_1_3_1_139_2","article-title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","author":"Howard Andrew G.","year":"2017","unstructured":"Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv 1704.04861 (2017).","journal-title":"arXiv 1704.04861"},{"key":"e_1_3_1_140_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2949343"},{"key":"e_1_3_1_141_2","doi-asserted-by":"publisher","DOI":"10.3390\/ani9070470"},{"key":"e_1_3_1_142_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3038184"},{"issue":"316","key":"e_1_3_1_143_2","article-title":"The status of labor-saving mechanization in US fruit and vegetable harvesting","volume":"27","author":"Huffman Wallace E.","year":"2012","unstructured":"Wallace E. Huffman. 2012. The status of labor-saving mechanization in US fruit and vegetable harvesting. Choices 27, 316-2016-6262 (2012).","journal-title":"Choices"},{"key":"e_1_3_1_144_2","article-title":"An Open Access Repository of Images on Plant Health to Enable the Development of Mobile Disease Diagnostics","author":"Hughes David","year":"2015","unstructured":"David Hughes, Marcel Salath\u00e9, et\u00a0al. 2015. An Open Access Repository of Images on Plant Health to Enable the Development of Mobile Disease Diagnostics. arXiv preprint arXiv:1511.08060 (2015).","journal-title":"arXiv preprint arXiv:1511.08060"},{"key":"e_1_3_1_145_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-021-05946-3"},{"key":"e_1_3_1_146_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00054"},{"key":"e_1_3_1_147_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.07.032"},{"key":"e_1_3_1_148_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiia.2019.06.001"},{"key":"e_1_3_1_149_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR\u201918)","author":"Jacobsen J\u00f6rn-Henrik","year":"2018","unstructured":"J\u00f6rn-Henrik Jacobsen, Arnold Smeulders, and Edouard Oyallon. 2018. i-RevNet: Deep invertible networks. In Proceedings of the International Conference on Learning Representations (ICLR\u201918)."},{"key":"e_1_3_1_150_2","article-title":"Perceiver IO: A General Architecture for Structured Inputs & Outputs","author":"Jaegle Andrew","year":"2021","unstructured":"Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier H\u00e9naff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, and Joao Carreira. 2021. Perceiver IO: A General Architecture for Structured Inputs & Outputs. arXiv preprint arXiv:2107.14795 (2021).","journal-title":"arXiv preprint arXiv:2107.14795"},{"key":"e_1_3_1_151_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Jiang Lu","year":"2020","unstructured":"Lu Jiang, Mason Liu Di Huang, and Weilong Yang. 2020. Beyond synthetic noise: Deep learning on controlled noisy labels. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_152_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jafr.2020.100033"},{"key":"e_1_3_1_153_2","article-title":"Webly Supervised Concept Expansion for General Purpose Vision Models","author":"Kamath Amita","year":"2022","unstructured":"Amita Kamath, Christopher Clark, Tanmay Gupta, Eric Kolve, Derek Hoiem, and Aniruddha Kembhavi. 2022. Webly Supervised Concept Expansion for General Purpose Vision Models. arXiv preprint arXiv:2202.02317 (2022).","journal-title":"arXiv preprint arXiv:2202.02317"},{"key":"e_1_3_1_154_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/395"},{"key":"e_1_3_1_155_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2018.09.011"},{"key":"e_1_3_1_156_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00963"},{"key":"e_1_3_1_157_2","first-page":"1885","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Koh Pang Wei","year":"2017","unstructured":"Pang Wei Koh and Percy Liang. 2017. Understanding black-box predictions via influence functions. In Proceedings of the International Conference on Machine Learning. PMLR, 1885\u20131894."},{"key":"e_1_3_1_158_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-019-09642-0"},{"key":"e_1_3_1_159_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3002345"},{"key":"e_1_3_1_160_2","doi-asserted-by":"publisher","DOI":"10.21273\/HORTSCI.50.9.1285"},{"key":"e_1_3_1_161_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106164"},{"key":"e_1_3_1_162_2","article-title":"Vertical farming: Singapore\u2019s solution to feed the local urban Population","author":"Krishnamurthy R.","year":"2014","unstructured":"R. Krishnamurthy. 2014. Vertical farming: Singapore\u2019s solution to feed the local urban Population. Permacult. Res. Instit. (2014). https:\/\/www.permaculturenews.org\/2014\/07\/25\/vertical-farming-singapores-solution-feed-local-urban-population\/","journal-title":"Permacult. Res. Instit."},{"key":"e_1_3_1_163_2","article-title":"One Weird Trick for Parallelizing Convolutional Neural Networks","author":"Krizhevsky Alex","year":"2014","unstructured":"Alex Krizhevsky. 2014. One Weird Trick for Parallelizing Convolutional Neural Networks. arXiv preprint arXiv:1404.5997 (2014).","journal-title":"arXiv preprint arXiv:1404.5997"},{"key":"e_1_3_1_164_2","first-page":"1097","volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS\u201912)","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS\u201912). Curran Associates Inc., Red Hook, NY, 1097\u20131105."},{"key":"e_1_3_1_165_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00328"},{"key":"e_1_3_1_166_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-013-9323-8"},{"key":"e_1_3_1_167_2","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21726"},{"key":"e_1_3_1_168_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459248"},{"key":"e_1_3_1_169_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2460697"},{"key":"e_1_3_1_170_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1744-7348.1991.tb04895.x"},{"key":"e_1_3_1_171_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR\u201917)","author":"Larsson Gustav","year":"2017","unstructured":"Gustav Larsson, Michael Maire, and Gregory Shakhnarovich. 2017. FractalNet: Ultra-deep neural networks without residuals. In Proceedings of the International Conference on Learning Representations (ICLR\u201917)."},{"key":"e_1_3_1_172_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"e_1_3_1_173_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_1_174_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_1_175_2","doi-asserted-by":"publisher","DOI":"10.3390\/horticulturae7090284"},{"key":"e_1_3_1_176_2","article-title":"Rationalizing Neural Predictions","author":"Lei Tao","year":"2016","unstructured":"Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016. Rationalizing Neural Predictions. arXiv preprint arXiv:1606.04155 (2016).","journal-title":"arXiv preprint arXiv:1606.04155"},{"key":"e_1_3_1_177_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-016-9443-z"},{"key":"e_1_3_1_178_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00368"},{"key":"e_1_3_1_179_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00719"},{"key":"e_1_3_1_180_2","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Li Yuanzhi","year":"2018","unstructured":"Yuanzhi Li and Yingyu Liang. 2018. Learning overparameterized neural networks via stochastic gradient descent on structured data. In Adv. Neural Inf. Process. Syst., S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc. Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/54fe976ba170c19ebae453679b362263-Paper.pdf"},{"key":"e_1_3_1_181_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.472"},{"key":"e_1_3_1_182_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.105174"},{"key":"e_1_3_1_183_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106055"},{"key":"e_1_3_1_184_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00276"},{"key":"e_1_3_1_185_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-019-09654-w"},{"key":"e_1_3_1_186_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19020428"},{"key":"e_1_3_1_187_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"e_1_3_1_188_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_3_1_189_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00674"},{"key":"e_1_3_1_190_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00633"},{"key":"e_1_3_1_191_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00304"},{"key":"e_1_3_1_192_2","volume-title":"Advances in Neural Information Processing Systems","author":"Liu Qiuhua","year":"2007","unstructured":"Qiuhua Liu, Xuejun Liao, and Lawrence Carin. 2007. Semi-supervised multitask learning. In Advances in Neural Information Processing Systems, J. Platt, D. Koller, Y. Singer, and S. Roweis (Eds.), Vol. 20. Curran Associates, Inc. Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2007\/file\/a34bacf839b923770b2c360eefa26748-Paper.pdf"},{"key":"e_1_3_1_193_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2018.03.007"},{"key":"e_1_3_1_194_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"e_1_3_1_195_2","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.01091"},{"key":"e_1_3_1_196_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_1_197_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_1_198_2","article-title":"Unified-IO: A Unified Model for Vision, Language, and Multi-modal Tasks","author":"Lu Jiasen","year":"2022","unstructured":"Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, and Aniruddha Kembhavi. 2022. 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Retrieved from https:\/\/ljvmiranda921.github.io\/notebook\/2021\/07\/30\/data-centric-ml\/"},{"key":"e_1_3_1_207_2","volume-title":"Interpretable Machine Learning","author":"Molnar Christoph","year":"2020","unstructured":"Christoph Molnar. 2020. Interpretable Machine Learning. Retrieved from https:\/\/christophm.github.io\/interpretable-ml-book\/"},{"key":"e_1_3_1_208_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_10"},{"key":"e_1_3_1_209_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICARCV.2010.5707436"},{"key":"e_1_3_1_210_2","unstructured":"Alexander Mordvintsev Christopher Olah and Mike Tyka. 2015. Inceptionism: Going deeper into neural networks. (2015). https:\/\/blog.research.google\/2015\/06\/inceptionism-going-deeper-into-neural.html?m=1"},{"key":"e_1_3_1_211_2","first-page":"15288","volume-title":"Advances in Neural Information Processing Systems","author":"Mukhoti Jishnu","year":"2020","unstructured":"Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, and Puneet Dokania. 2020. Calibrating deep neural networks using focal loss. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 15288\u201315299. Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/aeb7b30ef1d024a76f21a1d40e30c302-Paper.pdf"},{"key":"e_1_3_1_212_2","volume-title":"Advances in Neural Information Processing Systems","author":"M\u00fcller Rafael","year":"2019","unstructured":"Rafael M\u00fcller, Simon Kornblith, and Geoffrey E. Hinton. 2019. When does label smoothing help? In Advances in Neural Information Processing Systems., H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc. Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/f1748d6b0fd9d439f71450117eba2725-Paper.pdf"},{"key":"e_1_3_1_213_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41528-018-0039-8"},{"key":"e_1_3_1_214_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00904"},{"key":"e_1_3_1_215_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21030742"},{"key":"e_1_3_1_216_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41438-020-0323-3"},{"key":"e_1_3_1_217_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00161"},{"key":"e_1_3_1_218_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.07.048"},{"key":"e_1_3_1_219_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298668"},{"key":"e_1_3_1_220_2","doi-asserted-by":"publisher","DOI":"10.3390\/robotics7010011"},{"key":"e_1_3_1_221_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"e_1_3_1_222_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_1"},{"key":"e_1_3_1_223_2","first-page":"3592","article-title":"Learning global transparent models consistent with local contrastive explanations","volume":"33","author":"Pedapati Tejaswini","year":"2020","unstructured":"Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, and Amit Dhurandhar. 2020. Learning global transparent models consistent with local contrastive explanations. Adv. Neural Inf. Process. Syst. 33 (2020), 3592\u20133602.","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_1_224_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-06372-8"},{"key":"e_1_3_1_225_2","article-title":"Regularizing Neural Networks by Penalizing Confident Output Distributions","author":"Pereyra Gabriel","year":"2017","unstructured":"Gabriel Pereyra, George Tucker, Jan Chorowski, \u0141ukasz Kaiser, and Geoffrey Hinton. 2017. Regularizing Neural Networks by Penalizing Confident Output Distributions. arXiv 1701.06548 (2017).","journal-title":"arXiv 1701.06548"},{"key":"e_1_3_1_226_2","volume-title":"The Physiology of Plants: A Treatise upon the Metabolism and Sources of Energy in Plants","author":"Pfeffer Wilhelm","year":"1900","unstructured":"Wilhelm Pfeffer. 1900. The Physiology of Plants: A Treatise upon the Metabolism and Sources of Energy in Plants. Vol. 1. 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In Proceedings of the International Horticultural Congress: Nursery Crops; Development, Evaluation, Production and Use. 15\u201328."},{"key":"e_1_3_1_230_2","first-page":"19920","article-title":"Estimating training data influence by tracing gradient descent","volume":"33","author":"Pruthi Garima","year":"2020","unstructured":"Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan. 2020. Estimating training data influence by tracing gradient descent. Adv. Neural Inf. Process. Syst. 33 (2020), 19920\u201319930.","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_1_231_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2020.103435"},{"key":"e_1_3_1_232_2","unstructured":"Redmond R. Shamshiri Cornelia Weltzien Ibrahim A. Hameed Ian J. Yule Tony E. Grift Siva K. Balasundram Lenka Pitonakova Desa Ahmad and Girish Chowdhary. 2018. 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Trends in Food Science & Technology 121 (2022) 105\u2013113. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0924224421006609","DOI":"10.1016\/j.tifs.2021.12.003"},{"key":"e_1_3_1_235_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17040905"},{"key":"e_1_3_1_236_2","article-title":"You Only Look Once: Unified, Real-time Object Detection","author":"Redmon Joseph","year":"2016","unstructured":"Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You Only Look Once: Unified, Real-time Object Detection. arXiv 1506.02640 (2016).","journal-title":"arXiv 1506.02640"},{"key":"e_1_3_1_237_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"e_1_3_1_238_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.12.006"},{"key":"e_1_3_1_239_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.39"},{"key":"e_1_3_1_240_2","volume-title":"Advances in Neural Information Processing Systems","author":"Ren Shaoqing","year":"2015","unstructured":"Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems. 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Recurrent Neural Networks for Semantic Instance Segmentation. arXiv Preprint 1712.00617 (2017).","journal-title":"arXiv Preprint 1712.00617"},{"key":"e_1_3_1_253_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-015-0737-3"},{"key":"e_1_3_1_254_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3084358"},{"key":"e_1_3_1_255_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13007-019-0475-z"},{"key":"e_1_3_1_256_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_1_257_2","first-page":"130","volume-title":"Proceedings of the International Conference on Electrical Engineering and Informatics","volume":"1","author":"Seng Woo Chaw","year":"2009","unstructured":"Woo Chaw Seng and Seyed Hadi Mirisaee. 2009. A new method for fruits recognition system. In Proceedings of the International Conference on Electrical Engineering and Informatics, Vol. 1. IEEE, 130\u2013134."},{"key":"e_1_3_1_258_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystemseng.2015.12.003"},{"key":"e_1_3_1_259_2","article-title":"Overfeat: Integrated recognition, localization and detection using convolutional networks","author":"Sermanet Pierre","year":"2013","unstructured":"Pierre Sermanet, David Eigen, Xiang Zhang, Micha\u00ebl Mathieu, Rob Fergus, and Yann LeCun. 2013. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013).","journal-title":"arXiv preprint arXiv:1312.6229"},{"key":"e_1_3_1_260_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i15.17623"},{"key":"e_1_3_1_261_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3041316"},{"key":"e_1_3_1_262_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_29"},{"key":"e_1_3_1_263_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105214"},{"key":"e_1_3_1_264_2","article-title":"Rectified Meta-learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis","author":"Shi Ruifeng","year":"2020","unstructured":"Ruifeng Shi, Deming Zhai, Xianming Liu, Junjun Jiang, and Wen Gao. 2020. 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DOI:10.1109\/ISCON47742.2019.9036158","DOI":"10.1109\/ISCON47742.2019.9036158"},{"key":"e_1_3_1_269_2","doi-asserted-by":"publisher","DOI":"10.3390\/nu12061657"},{"key":"e_1_3_1_270_2","doi-asserted-by":"publisher","DOI":"10.1590\/0103-8478cr20151600"},{"key":"e_1_3_1_271_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature24270"},{"key":"e_1_3_1_272_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. 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On the Origin of Implicit Regularization in Stochastic Gradient Descent. arXiv preprint arXiv:2101.12176 (2021).","journal-title":"arXiv preprint arXiv:2101.12176"},{"key":"e_1_3_1_276_2","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell Jake","year":"2017","unstructured":"Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. Adv. Neural Inf. Process. Syst. 30 (2017).","journal-title":"Adv. Neural Inf. Process. 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Data augmentation and regularization in vision transformers. arXiv preprint arXiv:2106.10270."},{"key":"e_1_3_1_283_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.phyto.43.113004.133839"},{"key":"e_1_3_1_284_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.97"},{"key":"e_1_3_1_285_2","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture8120196"},{"key":"e_1_3_1_286_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106150"},{"key":"e_1_3_1_287_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"e_1_3_1_288_2","first-page":"3319","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Sundararajan Mukund","year":"2017","unstructured":"Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In Proceedings of the International Conference on Machine Learning. 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EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 6105\u20136114. Retrieved from https:\/\/proceedings.mlr.press\/v97\/tan19a.html"},{"key":"e_1_3_1_294_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc V. Le. 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 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Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/36ad8b5f42db492827016448975cc22d-Paper.pdf"},{"issue":"1","key":"e_1_3_1_299_2","first-page":"1","article-title":"Computer vision technology in agricultural automation\u2013A review","volume":"7","author":"Tian Hongkun","year":"2020","unstructured":"Hongkun Tian, Tianhai Wang, Yadong Liu, Xi Qiao, and Yanzhou Li. 2020. Computer vision technology in agricultural automation\u2013A review. Inf. Process. Agric. 7, 1 (2020), 1\u201319.","journal-title":"Inf. Process. 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Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2021\/file\/61f3a6dbc9120ea78ef75544826c814e-Paper.pdf"},{"key":"e_1_3_1_314_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00552"},{"key":"e_1_3_1_315_2","article-title":"Unifying Architectures, Tasks, and Modalities through a Simple Sequence-to-sequence learning Framework","author":"Wang Peng","year":"2022","unstructured":"Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022. 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Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/cd3afef9b8b89558cd56638c3631868a-Paper.pdf"},{"key":"e_1_3_1_319_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00928"},{"key":"e_1_3_1_320_2","doi-asserted-by":"publisher","DOI":"10.1145\/3386252"},{"key":"e_1_3_1_321_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19122742"},{"key":"e_1_3_1_322_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"e_1_3_1_323_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2014.07.001"},{"key":"e_1_3_1_324_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR\u201920)","author":"Wen Yeming","year":"2020","unstructured":"Yeming Wen, Dustin Tran, and Jimmy Ba. 2020. BatchEnsemble: An alternative approach to efficient ensemble and lifelong learning. 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LOGAN: Latent Optimisation for Generative Adversarial Networks. arXiv preprint arXiv:1912.00953 (2019).","journal-title":"arXiv preprint arXiv:1912.00953"},{"key":"e_1_3_1_334_2","article-title":"Gas exchange, growth and quality of guava seedlings under salt stress and salicylic acid","volume":"17","author":"Xavier Adnelba Vit\u00f3ria Oliveira","year":"2022","unstructured":"Adnelba Vit\u00f3ria Oliveira Xavier, Geovani Soares de Lima, Hans Raj Gheyi, Andr\u00e9 Alisson Rodrigues da Silva, Lauriane Almeida dos Anjos Soares, and Cassiano Nogueira de Lacerda. 2022. Gas exchange, growth and quality of guava seedlings under salt stress and salicylic acid. Revista Ambiente & \u00c1gua 17 (2022).","journal-title":"Revista Ambiente & \u00c1gua"},{"key":"e_1_3_1_335_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.15"},{"key":"e_1_3_1_336_2","article-title":"Horizontal and Vertical Ensemble with Deep Representation for Classification","author":"Xie Jingjing","year":"2013","unstructured":"Jingjing Xie, Bing Xu, and Zhang Chuang. 2013. Horizontal and Vertical Ensemble with Deep Representation for Classification. arXiv 1306.2759 (2013).","journal-title":"arXiv 1306.2759"},{"key":"e_1_3_1_337_2","article-title":"Aggregated Residual Transformations for Deep Neural Networks","author":"Xie Saining","year":"2016","unstructured":"Saining Xie, Ross B. Girshick, Piotr Doll\u00e1r, Zhuowen Tu, and Kaiming He. 2016. 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On the (in) fidelity and sensitivity of explanations. Adv. Neural Inf. Process. Syst. 32 (2019).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_1_342_2","article-title":"Representer point selection for explaining deep neural networks","volume":"31","author":"Yeh Chih-Kuan","year":"2018","unstructured":"Chih-Kuan Yeh, Joon Kim, Ian En-Hsu Yen, and Pradeep K. Ravikumar. 2018. Representer point selection for explaining deep neural networks. Adv. Neural Inf. Process. Syst. 31 (2018).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_1_343_2","doi-asserted-by":"publisher","DOI":"10.1071\/EA03185"},{"key":"e_1_3_1_344_2","article-title":"Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation","author":"Yeung Michael","year":"2021","unstructured":"Michael Yeung, Leonardo Rundo, Yang Nan, Evis Sala, Carola-Bibiane Sch\u00f6nlieb, and Guang Yang. 2021. Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation. arXiv preprint arXiv:2111.00528 (2021).","journal-title":"arXiv preprint arXiv:2111.00528"},{"key":"e_1_3_1_345_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/175"},{"key":"e_1_3_1_346_2","first-page":"12822","article-title":"Understanding interlocking dynamics of cooperative rationalization","volume":"34","author":"Yu Mo","year":"2021","unstructured":"Mo Yu, Yang Zhang, Shiyu Chang, and Tommi Jaakkola. 2021. Understanding interlocking dynamics of cooperative rationalization. Adv. Neural Inf. Process. Syst. 34 (2021), 12822\u201312835.","journal-title":"Adv. Neural Inf. Process. 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Understanding deep learning requires rethinking generalization. arXiv 1611.03530 (2017).","journal-title":"arXiv 1611.03530"},{"key":"e_1_3_1_356_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2016.2603342"},{"key":"e_1_3_1_357_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2899940"},{"key":"e_1_3_1_358_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2879324"},{"key":"e_1_3_1_359_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2879324"},{"key":"e_1_3_1_360_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41438-020-00345-6"},{"key":"e_1_3_1_361_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"e_1_3_1_362_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.03.012"},{"key":"e_1_3_1_363_2","doi-asserted-by":"publisher","DOI":"10.1002\/aepp.13115"},{"key":"e_1_3_1_364_2","volume-title":"Proceedings of the NeurIPS Conference","author":"Zhang Wenwei","year":"2021","unstructured":"Wenwei Zhang, Jiangmiao Pang, Kai Chen, and Chen Change Loy. 2021. 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