{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T03:28:28Z","timestamp":1775186908785,"version":"3.50.1"},"reference-count":205,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:00:00Z","timestamp":1740182400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:00:00Z","timestamp":1740182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s10115-025-02349-x","type":"journal-article","created":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T11:49:41Z","timestamp":1740224981000},"page":"4035-4085","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods"],"prefix":"10.1007","volume":"67","author":[{"given":"Alhassan","family":"Mumuni","sequence":"first","affiliation":[]},{"given":"Fuseini","family":"Mumuni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,22]]},"reference":[{"key":"2349_CR1","doi-asserted-by":"crossref","unstructured":"Kollias D (2023) Abaw: learning from synthetic data & multi-task learning challenges. In: European conference on computer vision. Springer, pp 157\u2013172","DOI":"10.1007\/978-3-031-25075-0_12"},{"key":"2349_CR2","doi-asserted-by":"crossref","unstructured":"Tabak J, Poli\u0107 M, Orsag M (2023) Towards synthetic data: dealing with the texture-bias in sim2real learning. In: intelligent autonomous systems 17: proceedings of the 17th international conference IAS-17. Springer, pp 630\u2013642","DOI":"10.1007\/978-3-031-22216-0_42"},{"key":"2349_CR3","doi-asserted-by":"publisher","first-page":"100546","DOI":"10.1016\/j.cosrev.2023.100546","volume":"48","author":"H Murtaza","year":"2023","unstructured":"Murtaza H, Ahmed M, Khan NF, Murtaza G, Zafar S, Bano A (2023) Synthetic data generation: state of the art in health care domain. Comput Sci Rev 48:100546","journal-title":"Comput Sci Rev"},{"key":"2349_CR4","doi-asserted-by":"crossref","unstructured":"Kwon O, Park J, Oh S (2023) Renderable neural radiance map for visual navigation. arXiv preprint arXiv:2303.00304","DOI":"10.1109\/CVPR52729.2023.00878"},{"key":"2349_CR5","doi-asserted-by":"crossref","unstructured":"Zhang M, Zheng S, Bao Z, Hebert M, Wang Y-X (2023) Beyond rgb: scene-property synthesis with neural radiance fields. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 795\u2013805","DOI":"10.1109\/WACV56688.2023.00086"},{"key":"2349_CR6","doi-asserted-by":"publisher","first-page":"107668","DOI":"10.1016\/j.compag.2023.107668","volume":"206","author":"MV da Silva","year":"2023","unstructured":"da Silva MV, Silva LH, Junior JDD, Escarpinati MC, Backes AR, Mari JF (2023) Generating synthetic multispectral images using neural style transfer: a study with application in channel alignment. Comput Electron Agric 206:107668","journal-title":"Comput Electron Agric"},{"key":"2349_CR7","doi-asserted-by":"crossref","unstructured":"Feng Y, Chandio BQ, Thomopoulos SI, Thompson M (2023) Variational autoencoders for generating synthetic tractography-based bundle templates in a low-data setting. bioRxiv, pp 2023\u201302","DOI":"10.1101\/2023.02.24.529954"},{"key":"2349_CR8","doi-asserted-by":"crossref","unstructured":"Wang R, Bashyam V, Yang Z, Yu F, Tassopoulou V, Chintapalli SS, Skampardoni I, Sreepada L, Sahoo D, Nikita K et al. (2023) Applications of generative adversarial networks in neuroimaging and clinical neuroscience. In: NeuroImage, p 119898","DOI":"10.1016\/j.neuroimage.2023.119898"},{"key":"2349_CR9","doi-asserted-by":"crossref","unstructured":"Suri S, Ilyas IF, R\u00e9 C, Rekatsinas T (2021) Ember: no-code context enrichment via similarity-based keyless joins. arXiv preprint arXiv:2106.01501","DOI":"10.14778\/3494124.3494149"},{"key":"2349_CR10","first-page":"1540","volume":"2021","author":"J Bai","year":"2021","unstructured":"Bai J, Wang J, Li Z, Ding D, Zhang J, Gao J (2021) Atj-net: auto-table-join network for automatic learning on relational databases. Proc Web Conf 2021:1540\u20131551","journal-title":"Proc Web Conf"},{"key":"2349_CR11","doi-asserted-by":"crossref","unstructured":"Li Y, Yu X, Koudas N (2021) Data acquisition for improving machine learning models. arXiv preprint arXiv:2105.14107","DOI":"10.14778\/3467861.3467872"},{"key":"2349_CR12","unstructured":"Lopes RG, Yin D, Poole B, Gilmer J, Cubuk ED (2019) Improving robustness without sacrificing accuracy with patch gaussian augmentation. arXiv preprint arXiv:1906.02611"},{"key":"2349_CR13","unstructured":"DeVries T, Taylor GW (2017) Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552"},{"key":"2349_CR14","first-page":"5402","volume":"34","author":"R Raileanu","year":"2021","unstructured":"Raileanu R, Goldstein M, Yarats D, Kostrikov I, Fergus R (2021) Automatic data augmentation for generalization in reinforcement learning. Adv Neural Inf Process Syst 34:5402\u20135415","journal-title":"Adv Neural Inf Process Syst"},{"key":"2349_CR15","unstructured":"Ravuri S, Vinyals O (2019) Seeing is not necessarily believing: Limitations of biggans for data augmentation"},{"key":"2349_CR16","doi-asserted-by":"crossref","unstructured":"Xue C, Yan J, Yan R, Chu SM, Hu Y, Lin Y (2019) Transferable automl by model sharing over grouped datasets. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9002\u20139011","DOI":"10.1109\/CVPR.2019.00921"},{"key":"2349_CR17","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV (2019) Autoaugment: learning augmentation strategies from data. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 113\u2013123","DOI":"10.1109\/CVPR.2019.00020"},{"key":"2349_CR18","doi-asserted-by":"crossref","unstructured":"Kim D, Koo J, Kim U-M (2022) A survey on automated machine learning: problems, methods and frameworks. In: International conference on human-computer interaction. Springer, pp 57\u201370","DOI":"10.1007\/978-3-031-05311-5_4"},{"key":"2349_CR19","doi-asserted-by":"publisher","first-page":"101822","DOI":"10.1016\/j.artmed.2020.101822","volume":"104","author":"J Waring","year":"2020","unstructured":"Waring J, Lindvall C, Umeton R (2020) Automated machine learning: review of the state-of-the-art and opportunities for healthcare. Artif Intell Med 104:101822","journal-title":"Artif Intell Med"},{"key":"2349_CR20","doi-asserted-by":"publisher","first-page":"106622","DOI":"10.1016\/j.knosys.2020.106622","volume":"212","author":"X He","year":"2021","unstructured":"He X, Zhao K, Chu X (2021) Automl: a survey of the state-of-the-art. Knowl-Based Syst 212:106622","journal-title":"Knowl-Based Syst"},{"key":"2349_CR21","doi-asserted-by":"crossref","unstructured":"Nagarajah T, Poravi G (2019) A review on automated machine learning (automl) systems. In: 2019 IEEE 5th international conference for convergence in technology (I2CT). IEEE, pp 1\u20136","DOI":"10.1109\/I2CT45611.2019.9033810"},{"key":"2349_CR22","doi-asserted-by":"crossref","unstructured":"Tuggener L, Amirian M, Rombach K, L\u00f6rwald S, Varlet A, Westermann C, Stadelmann T (2019) Automated machine learning in practice: state of the art and recent results. In: 2019 6th Swiss conference on data science (SDS). IEEE, pp 31\u201336","DOI":"10.1109\/SDS.2019.00-11"},{"issue":"8","key":"2349_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3470918","volume":"54","author":"SK Karmaker","year":"2021","unstructured":"Karmaker SK, Hassan MM, Smith MJ, Xu L, Zhai C, Veeramachaneni K (2021) Automl to date and beyond: challenges and opportunities. ACM Comput Surv (CSUR) 54(8):1\u201336","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"7","key":"2349_CR24","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1007\/s10115-023-01853-2","volume":"65","author":"Z Yang","year":"2023","unstructured":"Yang Z, Sinnott RO, Bailey J, Ke Q (2023) A survey of automated data augmentation algorithms for deep learning-based image classification tasks. Knowl Inf Syst 65(7):2805\u20132861","journal-title":"Knowl Inf Syst"},{"key":"2349_CR25","doi-asserted-by":"crossref","unstructured":"Cheung T-H, Yeung D-Y (2023) A survey of automated data augmentation for image classification: learning to compose, mix, and generate. In: IEEE transactions on neural networks and learning systems","DOI":"10.1109\/TNNLS.2023.3282258"},{"key":"2349_CR26","doi-asserted-by":"crossref","unstructured":"Hataya R, Zdenek J, Yoshizoe K, Nakayama H (2022) Meta approach to data augmentation optimization. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 2574\u20132583","DOI":"10.1109\/WACV51458.2022.00359"},{"key":"2349_CR27","doi-asserted-by":"crossref","unstructured":"Hataya R, Zdenek J, Yoshizoe K, Nakayama H (2022) Faster autoaugment: learning augmentation strategies using backpropagation. In: European conference on computer vision. Springer, pp 1\u201316","DOI":"10.1007\/978-3-030-58595-2_1"},{"key":"2349_CR28","doi-asserted-by":"crossref","unstructured":"Li Y, Hu G, Wang Y, Hospedales T, Robertson NM, Yang Y (2020) Differentiable automatic data augmentation. In: European conference on computer vision. Springer, pp 580\u2013595","DOI":"10.1007\/978-3-030-58542-6_35"},{"key":"2349_CR29","doi-asserted-by":"crossref","unstructured":"Mounsaveng S, Laradji I, Ben Ayed I, Vazquez D, Pedersoli M (2021) Learning data augmentation with online bilevel optimization for image classification. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 1691\u20131700","DOI":"10.1109\/WACV48630.2021.00173"},{"key":"2349_CR30","unstructured":"Miao H, Rahman LT (2020) Multi-class traffic sign classification using autoaugment and spatial transformer. CS230. Retrieved January 27, 2025, from https:\/\/cs230.stanford.edu\/projects_fall_2020\/reports\/55824835.pdf"},{"key":"2349_CR31","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Shlens J, Le QV (2020) Randaugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp 702\u2013703","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"2349_CR32","unstructured":"Kashima T, Yamada Y, Saito S (2020) Joint search of data augmentation policies and network architectures. arXiv preprint arXiv:2012.09407"},{"key":"2349_CR33","unstructured":"Lin S, Yu T, Feng R, Li X, Jin X, Chen Z (2021) Local patch autoaugment with multi-agent collaboration. arXiv e-prints, arXiv\u20132103"},{"key":"2349_CR34","unstructured":"Ho D, Liang E, Chen X, Stoica I, Abbeel (2019) Population based augmentation: efficient learning of augmentation policy schedules. In: International conference on machine learning. PMLR, pp 2731\u20132741"},{"key":"2349_CR35","doi-asserted-by":"publisher","first-page":"109347","DOI":"10.1016\/j.patcog.2023.109347","volume":"137","author":"M Xu","year":"2023","unstructured":"Xu M, Yoon S, Fuentes A, Park DS (2023) A comprehensive survey of image augmentation techniques for deep learning. Pattern Recognit 137:109347","journal-title":"Pattern Recognit"},{"key":"2349_CR36","doi-asserted-by":"crossref","unstructured":"Niu T, Bansal M (2019) Automatically learning data augmentation policies for dialogue tasks. arXiv preprint arXiv:1909.12868","DOI":"10.18653\/v1\/D19-1132"},{"key":"2349_CR37","doi-asserted-by":"crossref","unstructured":"Ren S, Zhang J, Li L, Sun X, Zhou J (2021) Text autoaugment: learning compositional augmentation policy for text classification. arXiv preprint arXiv:2109.00523","DOI":"10.18653\/v1\/2021.emnlp-main.711"},{"key":"2349_CR38","unstructured":"Dai H, Liu Z, Liao W, Huang X, Cao Y, Wu Z, Zhao L, Xu S, Liu W, Liu N, et al. (2023) Auggpt: Leveraging chatgpt for text data augmentation. arXiv preprint arXiv:2302.13007"},{"issue":"13","key":"2349_CR39","doi-asserted-by":"publisher","first-page":"2535","DOI":"10.3390\/electronics13132535","volume":"13","author":"H Zhao","year":"2024","unstructured":"Zhao H, Chen H, Ruggles TA, Feng Y, Singh D, Yoon H-J (2024) Improving text classification with large language model-based data augmentation. Electronics 13(13):2535","journal-title":"Electronics"},{"key":"2349_CR40","unstructured":"Tornede A, Deng D, Eimer T, Giovanelli J, Mohan A, Ruhkopf T, Segel S, Theodorakopoulos D, Tornede T, Wachsmuth H et al., (2023) Automl in the age of large language models: current challenges, future opportunities and risks. arXiv preprint arXiv:2306.08107"},{"key":"2349_CR41","unstructured":"Margeloiu A, Bazaga A, Simidjievski N, Li\u00f2, Jamnik M (2024) Tabmda: tabular manifold data augmentation for any classifier using transformers with in-context subsetting. arXiv preprint arXiv:2406.01805"},{"key":"2349_CR42","doi-asserted-by":"crossref","unstructured":"Zhang S, Balog K (2019) Auto-completion for data cells in relational tables. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 761\u2013770","DOI":"10.1145\/3357384.3357932"},{"key":"2349_CR43","doi-asserted-by":"crossref","unstructured":"Fang J, Tang C, Cui Q, Zhu F, Li L, Zhou J, Zhu W (2022) Semi-supervised learning with data augmentation for tabular data. In: Proceedings of the 31st ACM international conference on information & knowledge management, pp 3928\u20133932","DOI":"10.1145\/3511808.3557699"},{"key":"2349_CR44","doi-asserted-by":"crossref","unstructured":"Chepurko N, Marcus R, Zgraggen E, Fernandez RC, Kraska T, Karger D (2020) Arda: automatic relational data augmentation for machine learning. arXiv preprint arXiv:2003.09758","DOI":"10.14778\/3397230.3397235"},{"key":"2349_CR45","doi-asserted-by":"crossref","unstructured":"Bazrafkan S, Nedelcu T, Filipczuk, Corcoran, (2017) Deep learning for facial expression recognition: A step closer to a smartphone that knows your moods. In: 2017 IEEE international conference on consumer electronics (ICCE). IEEE, 217\u2013220","DOI":"10.1109\/ICCE.2017.7889290"},{"key":"2349_CR46","doi-asserted-by":"crossref","unstructured":"Gao B, Gouk H, Hospedales TM (2021) Searching for robustness: loss learning for noisy classification tasks. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6670\u20136679","DOI":"10.1109\/ICCV48922.2021.00660"},{"key":"2349_CR47","unstructured":"Gao B (2023) Meta-learning to optimise: loss functions and update rules. [Doctoral dissertation, The University of Edinburgh]. Edinburgh Research Archive. https:\/\/era.ed.ac.uk\/handle\/1842\/39821?show=full"},{"key":"2349_CR48","first-page":"22919","volume":"34","author":"Y-Y Kim","year":"2021","unstructured":"Kim Y-Y, Song K, Jang J, Moon I-C (2021) Lada: look-ahead data acquisition via augmentation for deep active learning. Adv Neural Inf Process Syst 34:22919\u201322930","journal-title":"Adv Neural Inf Process Syst"},{"key":"2349_CR49","unstructured":"Yao Q, Yang H, Han B, Niu G, Kwok JT-Y (2020) Searching to exploit memorization effect in learning with noisy labels. In: International conference on machine learning. PMLR, pp 10789\u201310798"},{"key":"2349_CR50","unstructured":"Shu J, Yuan X, Meng D, Xu Z (2023) Dac-mr: data augmentation consistency based meta-regularization for meta-learning. arXiv preprint arXiv:2305.07892"},{"key":"2349_CR51","unstructured":"Gao C, Liu C, Shu J, Liu F, Liu J, Yang L, Gao X, Meng D (2024) \u201cAre dense labels always necessary for 3d object detection from point cloud?\u201d. arXiv preprint arXiv:2403.02818"},{"key":"2349_CR52","unstructured":"Gao B, Gouk H, Yang Y, Hospedales T (2022) Loss function learning for domain generalization by implicit gradient. In: International conference on machine learning. PMLR, pp 7002\u20137016"},{"key":"2349_CR53","doi-asserted-by":"crossref","unstructured":"Kumar A, Naughton J, Patel JM (2015) Learning generalized linear models over normalized data. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data, pp 1969\u20131984","DOI":"10.1145\/2723372.2723713"},{"key":"2349_CR54","unstructured":"Esmailoghli M, Quian\u00e9-Ruiz J-A, Abedjan Z (2021) Cocoa: correlation coefficient-aware data augmentation. In: EDBT, pp 331\u2013336"},{"key":"2349_CR55","doi-asserted-by":"crossref","unstructured":"Koutras C, Siachamis G, Ionescu A, Psarakis K, Brons J, Fragkoulis M, Lofi C, Bonifati A, Katsifodimos A (2021) Valentine: evaluating matching techniques for dataset discovery. In: 2021 IEEE 37th international conference on data engineering (ICDE). IEEE, pp 468\u2013479","DOI":"10.1109\/ICDE51399.2021.00047"},{"key":"2349_CR56","doi-asserted-by":"crossref","unstructured":"Li G, Zhou X, Cao L (2021) Ai meets database: Ai4db and db4ai. In: Proceedings of the 2021 international conference on management of data, pp 2859\u20132866","DOI":"10.1145\/3448016.3457542"},{"issue":"1","key":"2349_CR57","first-page":"1997","volume":"20","author":"T Elsken","year":"2019","unstructured":"Elsken T, Metzen JH, Hutter F (2019) Neural architecture search: a survey. J Mach Learn Res 20(1):1997\u20132017","journal-title":"J Mach Learn Res"},{"key":"2349_CR58","doi-asserted-by":"crossref","unstructured":"Kumar A, Naughton J, Patel JM, Zhu X (2016) To join or not to join? Thinking twice about joins before feature selection. In: Proceedings of the 2016 international conference on management of data, pp 19\u201334","DOI":"10.1145\/2882903.2882952"},{"issue":"2","key":"2349_CR59","doi-asserted-by":"publisher","first-page":"24","DOI":"10.3390\/computers10020024","volume":"10","author":"A Mustafa","year":"2021","unstructured":"Mustafa A, Rahimi Azghadi M (2021) Automated machine learning for healthcare and clinical notes analysis. Computers 10(2):24","journal-title":"Computers"},{"key":"2349_CR60","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.future.2021.08.022","volume":"127","author":"NO Nikitin","year":"2022","unstructured":"Nikitin NO, Vychuzhanin Sarafanov M, Polonskaia IS, Revin I, Barabanova IV, Maximov G, Kalyuzhnaya AV, Boukhanovsky A (2022) Automated evolutionary approach for the design of composite machine learning pipelines. Futur Gener Comput Syst 127:109\u2013125","journal-title":"Futur Gener Comput Syst"},{"key":"2349_CR61","unstructured":"Shi X, Mueller J, Erickson N, Li M, Smola A (2021) Multimodal automl on structured tables with text fields. In: 8th ICML workshop on automated machine learning (AutoML)"},{"key":"2349_CR62","doi-asserted-by":"crossref","unstructured":"Erickson N, Shi X, Sharpnack J, Smola A (2022) Multimodal automl for image, text and tabular data. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 4786\u20134787","DOI":"10.1145\/3534678.3542616"},{"key":"2349_CR63","unstructured":"Erickson N, Mueller J, Shirkov A, Zhang H, Larroy, Li., Smola A (2020) Autogluon-tabular: Robust and accurate automl for structured data. In: arXiv preprint arXiv:2003.06505"},{"key":"2349_CR64","doi-asserted-by":"crossref","unstructured":"Nargesian F, Asudeh A, Jagadish H (2022) Responsible data integration: next-generation challenges. In: Proceedings of the 2022 international conference on management of data, pp 2458\u20132464","DOI":"10.1145\/3514221.3522567"},{"issue":"7","key":"2349_CR65","first-page":"1466","volume":"15","author":"C Chai","year":"2022","unstructured":"Chai C, Liu J, Tang N, Li G, Luo Y (2022) Selective data acquisition in the wild for model charging. PVLDB 15(7):1466\u20131478","journal-title":"PVLDB"},{"key":"2349_CR66","doi-asserted-by":"crossref","unstructured":"Shende MK, Feijoo-Lorenzo AE, Bokde ND (2022) cleanTS: Automated (AutoML) tool to clean univariate time series at microscales. Neurocomputing 500:155\u2013176","DOI":"10.1016\/j.neucom.2022.05.057"},{"key":"2349_CR67","doi-asserted-by":"crossref","unstructured":"Mangrulkar A, Rane S, Sunnapwar V (2020) Image-based bio-cad modeling: overview, scope, and challenges. J Phys Conf ser 1706:012189. https:\/\/doi.org\/10.1088\/1742-6596\/1706\/1\/012189","DOI":"10.1088\/1742-6596\/1706\/1\/012189"},{"key":"2349_CR68","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-030-75178-4","volume-title":"Synthetic data for deep learning","author":"SI Nikolenko","year":"2021","unstructured":"Nikolenko SI (2021) Synthetic data for deep learning. Springer, Germany, p 174"},{"key":"2349_CR69","doi-asserted-by":"crossref","unstructured":"de Melo CM, Torralba A, Guibas L, DiCarlo J, Chellappa R, Hodgins J (2021) Next-generation deep learning based on simulators and synthetic data. Trends Cognit Sci","DOI":"10.1016\/j.tics.2021.11.008"},{"key":"2349_CR70","doi-asserted-by":"crossref","unstructured":"Tremblay J, Prakash A, Acuna D, Brophy M, Jampani V, Anil C, To T, Cameracci E, Boochoon S, Birchfield S (2018) Training deep networks with synthetic data: Bridging the reality gap by domain randomization. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 969\u2013977","DOI":"10.1109\/CVPRW.2018.00143"},{"issue":"1\u20134","key":"2349_CR71","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1080\/16864360.2005.10738392","volume":"2","author":"B Starly","year":"2005","unstructured":"Starly B, Fang Z, Sun W, Shokoufandeh A, Regli W (2005) Three-dimensional reconstruction for medical-cad modeling. Comput-Aided Des Appl 2(1\u20134):431\u2013438","journal-title":"Comput-Aided Des Appl"},{"key":"2349_CR72","unstructured":"Chang AX, Funkhouser T, Guibas L, Hanrahan, Huang Q, Li Z, Savarese S, Savva M, Song S, Su H et al. (2015) Shapenet: an information-rich 3d model repository. arXiv preprint arXiv:1512.03012"},{"key":"2349_CR73","doi-asserted-by":"crossref","unstructured":"Mishra S, Panda R, Phoo C, Chen C-FR, Karlinsky L, Saenko K, Saligrama V, Feris RS (2022) Task2sim: towards effective pre-training and transfer from synthetic data. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9194\u20139204","DOI":"10.1109\/CVPR52688.2022.00898"},{"key":"2349_CR74","doi-asserted-by":"crossref","unstructured":"Wang Y, Mu N, Grandi D, Savva N, Steinhardt J (2022) A3d: studying pretrained representations with programmable datasets. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4878\u20134889","DOI":"10.1109\/CVPRW56347.2022.00535"},{"key":"2349_CR75","doi-asserted-by":"crossref","unstructured":"Du Y, Watkins O, Darrell T, Abbeel, Pathak (2021) Auto-tuned sim-to-real transfer. In: 2021 IEEE international conference on robotics and automation (ICRA). IEEE, pp 1290\u20131296","DOI":"10.1109\/ICRA48506.2021.9562091"},{"key":"2349_CR76","unstructured":"Thompson JL (2022) Augmenting biological pathway extraction with synthetic data and active learning, Ph.D. dissertation, University of Missouri\u2013Columbia"},{"issue":"9","key":"2349_CR77","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.1177\/14680874211023466","volume":"23","author":"O Owoyele","year":"2022","unstructured":"Owoyele O, Pal P, Vidal Torreira A, Probst D, Shaxted M, Wilde M, Senecal PK (2022) Application of an automated machine learning-genetic algorithm (automl-ga) coupled with computational fluid dynamics simulations for rapid engine design optimization. Int J Engine Res 23(9):1586\u20131601","journal-title":"Int J Engine Res"},{"issue":"3","key":"2349_CR78","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/BF00992696","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams RJ (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn 8(3):229\u2013256","journal-title":"Mach Learn"},{"key":"2349_CR79","doi-asserted-by":"crossref","unstructured":"Behl HS, Baydin AG, Gal R, Torr H, Vineet V (2020) Autosimulate:(quickly) learning synthetic data generation. In: European conference on computer vision. Springer, pp 255\u2013271","DOI":"10.1007\/978-3-030-58542-6_16"},{"key":"2349_CR80","unstructured":"Ruiz N, Schulter S, Chandraker M (2018) Learning to simulate. In: arXiv preprint arXiv:1810.02513"},{"key":"2349_CR81","first-page":"14650","volume":"33","author":"S Shirobokov","year":"2020","unstructured":"Shirobokov S, Belavin V, Kagan M, Ustyuzhanin A, Baydin AG (2020) Black-box optimization with local generative surrogates. Adv Neural Inf Process Syst 33:14650\u201314662","journal-title":"Adv Neural Inf Process Syst"},{"key":"2349_CR82","doi-asserted-by":"crossref","unstructured":"Sun D, Vlasic D, Herrmann C, Jampani V, Krainin M, Chang H, Zabih R, Freeman WT, Liu C (2021) Autoflow: learning a better training set for optical flow. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10093\u201310102","DOI":"10.1109\/CVPR46437.2021.00996"},{"key":"2349_CR83","doi-asserted-by":"crossref","unstructured":"Kar A, Prakash A, Liu M-Y, Cameracci E, Yuan J, Rusiniak M, Acuna D, Torralba A, Fidler S (2019) Meta-sim: learning to generate synthetic datasets. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 4551\u20134560","DOI":"10.1109\/ICCV.2019.00465"},{"key":"2349_CR84","doi-asserted-by":"crossref","unstructured":"Ge Y, Behl H, Xu J, Gunasekar S, Joshi, Song Y, Wang X, Itti L, Vineet V (2022) Neural-sim: learning to generate training data with nerf. In: European conference on computer vision. Springer, pp 477\u2013493","DOI":"10.1007\/978-3-031-20050-2_28"},{"key":"2349_CR85","doi-asserted-by":"crossref","unstructured":"Han Y, Luo K, Luo A, Liu J, Fan H, Luo G, Liu S (2022) Realflow: Em-based realistic optical flow dataset generation from videos. In: European conference on computer vision. Springer, pp 288\u2013305","DOI":"10.1007\/978-3-031-19800-7_17"},{"key":"2349_CR86","doi-asserted-by":"crossref","unstructured":"Kortylewski A, Egger B, Schneider A, Gerig T, Morel-Forster A, Vetter T (2019) Analyzing and reducing the damage of dataset bias to face recognition with synthetic data. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops","DOI":"10.1109\/CVPRW.2019.00279"},{"key":"2349_CR87","unstructured":"Ebadi SE, Dhakad S, Vishwakarma S, Wang C, Jhang Y-C, Chociej M, Crespi A, Thaman A, Ganguly S (2022) Psp-hdri $$+ $$: a synthetic dataset generator for pre-training of human-centric computer vision models. arXiv preprint arXiv:2207.05025"},{"key":"2349_CR88","doi-asserted-by":"crossref","unstructured":"Mayer N, Ilg E, Hausser, Fischer, Cremers D, Dosovitskiy A, Brox T (2016) A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4040\u20134048","DOI":"10.1109\/CVPR.2016.438"},{"key":"2349_CR89","doi-asserted-by":"crossref","unstructured":"Dosovitskiy A, Fischer, Ilg E, Hausser, Hazirbas C, Golkov V, Van Der Smagt, Cremers D, Brox T (2015) Flownet: learning optical flow with convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 2758\u20132766","DOI":"10.1109\/ICCV.2015.316"},{"key":"2349_CR90","doi-asserted-by":"crossref","unstructured":"Butler DJ, Wulff J, Stanley GB, Black MJ (2012) A naturalistic open source movie for optical flow evaluation. In: European conference on computer vision. Springer, pp 611\u2013625","DOI":"10.1007\/978-3-642-33783-3_44"},{"key":"2349_CR91","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz, Urtasun R (2012) Are we ready for autonomous driving? The kitti vision benchmark suite. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 3354\u20133361","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"2349_CR92","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer W (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"issue":"9","key":"2349_CR93","doi-asserted-by":"publisher","first-page":"135","DOI":"10.3390\/data8090135","volume":"8","author":"W Wang","year":"2023","unstructured":"Wang W, Pai T-W (2023) enhancing small tabular clinical trial dataset through hybrid data augmentation: combining smote and wcgan-gp. Data 8(9):135","journal-title":"Data"},{"issue":"9","key":"2349_CR94","doi-asserted-by":"publisher","first-page":"6390","DOI":"10.1109\/TNNLS.2021.3136503","volume":"34","author":"D Dablain","year":"2022","unstructured":"Dablain D, Krawczyk B, Chawla NV (2022) Deepsmote: fusing deep learning and smote for imbalanced data. IEEE Trans Neural Netw Learn Syst 34(9):6390\u20136404","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2349_CR95","doi-asserted-by":"crossref","unstructured":"Arag\u00e3o MVC, de Freitas Carvalho M, de Morais Pereira T, de Figueiredo FA, Mafra SB (2024) Enhancing AutoML performance for imbalanced tabular data classification: a self-balancing pipeline. Res Sq. https:\/\/doi.org\/10.21203\/rs.3.rs-4469436\/v1 (preprint)","DOI":"10.21203\/rs.3.rs-4469436\/v1"},{"key":"2349_CR96","doi-asserted-by":"crossref","unstructured":"Rashidi H, Albahra S, Rubin B, Hu B (2023) STNG (Synthetic Tabular Neural Generator): a novel and fully automated platform for synthetic tabular data generation and validation. Res Sq. https:\/\/doi.org\/10.21203\/rs.3.rs-3716775\/v1 (preprint)","DOI":"10.21203\/rs.3.rs-3716775\/v1"},{"key":"2349_CR97","doi-asserted-by":"crossref","unstructured":"Xu S, Semnani SJ, Campagna G, Lam MS (2020) Autoqa: from databases to qa semantic parsers with only synthetic training data. arXiv preprint arXiv:2010.04806","DOI":"10.18653\/v1\/2020.emnlp-main.31"},{"key":"2349_CR98","unstructured":"Li Z, Si L, Guo C, Yang Y, Cao Q (2024) Data augmentation for text-based person retrieval using large language models. arXiv preprint arXiv:2405.11971"},{"key":"2349_CR99","doi-asserted-by":"crossref","unstructured":"Glazkova A, Zakharova O (2024) Evaluating llm prompts for data augmentation in multi-label classification of ecological texts. arXiv preprint arXiv:2411.14896","DOI":"10.1109\/ISPRAS64596.2024.10899128"},{"key":"2349_CR100","unstructured":"Xu J, Li J, Liu Z, Suryanarayanan NAV, Zhou G, Guo J, Iba H, Tei K (2024) Large language models synergize with automated machine learning. arXiv preprint arXiv:2405.03727"},{"key":"2349_CR101","doi-asserted-by":"crossref","unstructured":"Ma L, Li N, Yu G, Geng X, Cheng S, Wang X, Huang M, Jin Y (2023) Pareto-wise ranking classifier for multiobjective evolutionary neural architecture search. IEEE Trans Evolut Comput 28(3):570\u2013581. https:\/\/doi.org\/10.1109\/TEVC.2023.3314766","DOI":"10.1109\/TEVC.2023.3314766"},{"key":"2349_CR102","doi-asserted-by":"publisher","unstructured":"Ma L, Kang H, Yu G, Li Q, He Q (2024) Single-domain generalized predictor for neural architecture search system. IEEE Trans Comput 73(5):1400\u20131413. https:\/\/doi.org\/10.1109\/TC.2024.3365949","DOI":"10.1109\/TC.2024.3365949"},{"key":"2349_CR103","doi-asserted-by":"crossref","unstructured":"Sun Y, Xue B, Zhang M, Yen GG, Lv J (2020) Automatically designing cnn architectures using the genetic algorithm for image classification. IEEE Trans Cybernet 50(9):3840\u20133854","DOI":"10.1109\/TCYB.2020.2983860"},{"key":"2349_CR104","doi-asserted-by":"crossref","unstructured":"Liu A, Huang Z, Huang Z, Wang N (2021) Direct differentiable augmentation search. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 12219\u201312228","DOI":"10.1109\/ICCV48922.2021.01200"},{"key":"2349_CR105","unstructured":"Lim S, Kim I, Kim T,Kim C, Kim S (2019) Fast autoaugment. In: Advances in neural information processing systems, vol 32"},{"key":"2349_CR106","doi-asserted-by":"crossref","unstructured":"Lu S, Zhao M, Yuan S, Wang X, Yang L, Niu D (2023) Bda: bandit-based transferable autoaugment. In: Proceedings of the 2023 SIAM international conference on data mining (SDM). SIAM, pp 550\u2013558","DOI":"10.1137\/1.9781611977653.ch62"},{"key":"2349_CR107","unstructured":"Zhang X, Wang Q, Zhang J, Zhong Z (2019) Adversarial autoaugment. arXiv preprint arXiv:1912.11188"},{"key":"2349_CR108","unstructured":"Krizhevsky A (2009) Imagenet classification with deep convolutional neural networks. Master\u2019s thesis, University of Toronto. https:\/\/www.cs.utoronto.ca\/~kriz\/learning-features-2009-TR.pdf"},{"key":"2349_CR109","unstructured":"Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY (2011) Reading digits in natural images with unsupervised feature learning"},{"key":"2349_CR110","doi-asserted-by":"crossref","unstructured":"Wei L, Xiao A, Xie L, Zhang X, Chen X, Tian Q (2020) Circumventing outliers of autoaugment with knowledge distillation. In: European conference on computer vision. Springer, pp 608\u2013625","DOI":"10.1007\/978-3-030-58580-8_36"},{"key":"2349_CR111","doi-asserted-by":"crossref","unstructured":"Lin C, Guo M, Li C, Yuan X, Wu W, Yan J, Lin D, Ouyang W (2019) Online hyper-parameter learning for auto-augmentation strategy. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6579\u20136588","DOI":"10.1109\/ICCV.2019.00668"},{"key":"2349_CR112","doi-asserted-by":"crossref","unstructured":"Gao Y, Tang Z, Zhou M, Metaxas D (2021) Enabling data diversity: efficient automatic augmentation via regularized adversarial training. In: International conference on information processing in medical imaging. Springer, pp 85\u201397","DOI":"10.1007\/978-3-030-78191-0_7"},{"key":"2349_CR113","doi-asserted-by":"crossref","unstructured":"Zhao A, Balakrishnan G, Durand F, Guttag JV, Dalca AV (2019) Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8543\u20138553","DOI":"10.1109\/CVPR.2019.00874"},{"key":"2349_CR114","unstructured":"Jaderberg M, Simonyan K, Zisserman A (2015) Spatial transformer networks. In: Advances in neural information processing systems, vol 28"},{"key":"2349_CR115","doi-asserted-by":"crossref","unstructured":"Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 764\u2013773","DOI":"10.1109\/ICCV.2017.89"},{"key":"2349_CR116","doi-asserted-by":"crossref","unstructured":"Chu C-T, Rohmatillah M, Lee C-H, Chien J-T (2022) Augmentation strategy optimization for language understanding. In: ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 7952\u20137956","DOI":"10.1109\/ICASSP43922.2022.9746696"},{"key":"2349_CR117","doi-asserted-by":"crossref","unstructured":"Tang Z, Gao Y, Karlinsky L, Sattigeri, Feris R, Metaxas D (2020) Onlineaugment: online data augmentation with less domain knowledge. In: European conference on computer vision. Springer, pp 313\u2013329","DOI":"10.1007\/978-3-030-58571-6_19"},{"key":"2349_CR118","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV (2018) Autoaugment: learning augmentation policies from data. arXiv preprint arXiv:1805.09501","DOI":"10.1109\/CVPR.2019.00020"},{"key":"2349_CR119","unstructured":"Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2017) mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412"},{"key":"2349_CR120","doi-asserted-by":"crossref","unstructured":"Hu T-Y, Shrivastava A, Chang J-HR, Koppula H, Braun S, Hwang K, Kalinli O, Tuzel O (2021) Sapaugment: learning a sample adaptive policy for data augmentation. In: ICASSP 2021-2021 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 4040\u20134044","DOI":"10.1109\/ICASSP39728.2021.9413928"},{"key":"2349_CR121","unstructured":"Inoue H (2018) Data augmentation by pairing samples for images classification. arXiv preprint arXiv:1801.02929"},{"key":"2349_CR122","doi-asserted-by":"crossref","unstructured":"Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y (2019) Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6023\u20136032","DOI":"10.1109\/ICCV.2019.00612"},{"key":"2349_CR123","doi-asserted-by":"publisher","first-page":"108637","DOI":"10.1016\/j.patcog.2022.108637","volume":"127","author":"V Chinbat","year":"2022","unstructured":"Chinbat V, Bae S-H (2022) Ga3n: generative adversarial autoaugment network. Pattern Recogn 127:108637","journal-title":"Pattern Recogn"},{"key":"2349_CR124","doi-asserted-by":"crossref","unstructured":"Peng X, Tang Z, Yang F, Feris RS, Metaxas D (2018) Jointly optimize data augmentation and network training: Adversarial data augmentation in human pose estimation. In: Proceedings of the IEEE onference on Computer Vision and Pattern Recognition, pp 2226\u20132234","DOI":"10.1109\/CVPR.2018.00237"},{"key":"2349_CR125","unstructured":"Liu S, Lu S, Chen X, Feng Y, Xu K, Al-Dujaili, Hong M, O\u2019Reilly (2019) Min-max optimization without gradients: Convergence and applications to adversarial ml. arXiv preprint arXiv:1909.13806"},{"key":"2349_CR126","unstructured":"Lee DJ-L, Macke S (2020) A human-in-the-loop perspective on automl: milestones and the road ahead. IEEE Data Eng Bull"},{"key":"2349_CR127","doi-asserted-by":"crossref","unstructured":"Li Y, Wang Z, Xie Y, Ding B, Zeng K, Zhang C (2021) Automl: from methodology to application. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 4853\u20134856","DOI":"10.1145\/3459637.3483279"},{"key":"2349_CR128","unstructured":"Zheng Yu, Zhang Zhi, Yan Shen, Zhang Mi (2022) Deep Autoaugment. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=St-53J9ZARf"},{"key":"2349_CR129","doi-asserted-by":"crossref","unstructured":"Liu Z, Jin H, Wang T-H, Zhou K, Hu X (2021) Divaug: plug-in automated data augmentation with explicit diversity maximization. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 4762\u20134770","DOI":"10.1109\/ICCV48922.2021.00472"},{"key":"2349_CR130","doi-asserted-by":"crossref","unstructured":"Chen Y, Zhang, Kong T, Li Y, Zhang X, Qi L, Sun J, Jia J (2022) Scale-aware automatic augmentations for object detection with dynamic training. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2022.3166905"},{"issue":"12","key":"2349_CR131","first-page":"11097","volume":"35","author":"F Zhou","year":"2021","unstructured":"Zhou F, Li J, Xie C, Chen F, Hong L, Sun R, Li Z (2021) Metaaugment: sample-aware data augmentation policy learning. Proc AAAI Conf Artif Intell 35(12):11097\u201311105","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"2349_CR132","unstructured":"Cheung T-H, Yeung D-Y (2021) Adaaug: learning class-and instance-adaptive data augmentation policies. In: International conference on learning representations"},{"key":"2349_CR133","unstructured":"Miao N, Rainforth T, Mathieu E, Dubois Y, Teh YW, Foster A, Kim H (2023) Learning instance-specific augmentations by capturing local invariances"},{"key":"2349_CR134","doi-asserted-by":"publisher","first-page":"26393","DOI":"10.1109\/ACCESS.2023.3258179","volume":"11","author":"J Yoo","year":"2023","unstructured":"Yoo J, Kang S (2023) Class-adaptive data augmentation for image classification. IEEE Access 11:26393\u201326402","journal-title":"IEEE Access"},{"key":"2349_CR135","doi-asserted-by":"crossref","unstructured":"Yang D, Myronenko A, Wang X, Xu Z, Roth HR, Xu D (2021) T-automl: automated machine learning for lesion segmentation using transformers in 3d medical imaging. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3962\u20133974","DOI":"10.1109\/ICCV48922.2021.00393"},{"key":"2349_CR136","doi-asserted-by":"crossref","unstructured":"Lopes V, Gaspar A, Alexandre LA, Cordeiro J (2021) An automl-based approach to multimodal image sentiment analysis. In: 2021 international joint conference on neural networks (IJCNN). IEEE, pp 1\u20139","DOI":"10.1109\/IJCNN52387.2021.9533552"},{"key":"2349_CR137","doi-asserted-by":"crossref","unstructured":"Chu X, He X (2022) Medpipe: end-to-end joint search of data augmentation policy and neural architecture for 3d medical image classification","DOI":"10.36227\/techrxiv.19513780.v1"},{"key":"2349_CR138","unstructured":"Liu H, Simonyan K, Yang Y (2018) Darts: differentiable architecture search. arXiv preprint arXiv:1806.09055"},{"key":"2349_CR139","unstructured":"Snoek J, Larochelle H, Adams R (2012) Practical bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems, vol 25"},{"issue":"1","key":"2349_CR140","first-page":"B1","volume":"97","author":"T B\u00e4ck","year":"1997","unstructured":"B\u00e4ck T, Fogel DB, Michalewicz Z (1997) Handbook of evolutionary computation. Release 97(1):B1","journal-title":"Release"},{"key":"2349_CR141","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1613\/jair.301","volume":"4","author":"L Kaelbling","year":"1996","unstructured":"Kaelbling L, Littman ML, Moore AW (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237\u2013285","journal-title":"J Artif Intell Res"},{"key":"2349_CR142","first-page":"19088","volume":"33","author":"K Tian","year":"2020","unstructured":"Tian K, Lin C, Sun Zhou L, Yan J, Ouyang W (2020) Improving auto-augment via augmentation-wise weight sharing. Adv Neural Inf Process Syst 33:19088\u201319098","journal-title":"Adv Neural Inf Process Syst"},{"key":"2349_CR143","doi-asserted-by":"crossref","unstructured":"Gowda SN, Rohrbach M, Keller F, Sevilla-Lara L (2022) Learn2augment: learning to composite videos for data augmentation in action recognition. In:European conference on computer vision. Springer, pp 242\u2013259","DOI":"10.1007\/978-3-031-19821-2_14"},{"issue":"11","key":"2349_CR144","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1057\/jors.1993.181","volume":"44","author":"DJ White","year":"1993","unstructured":"White DJ (1993) A survey of applications of Markov decision processes. J Oper Res Soc 44(11):1073\u20131096","journal-title":"J Oper Res Soc"},{"key":"2349_CR145","doi-asserted-by":"crossref","unstructured":"Spaan MT (2012) Partially observable markov decision processes. In: Reinforcement learning. Springer, pp 387\u2013414","DOI":"10.1007\/978-3-642-27645-3_12"},{"key":"2349_CR146","first-page":"1","volume":"53","author":"C Zhang","year":"2022","unstructured":"Zhang C, Li X, Zhang Z, Cui J, Yang B (2022) Bo-aug: learning data augmentation policies via bayesian optimization. Appl Intell 53:1\u201316","journal-title":"Appl Intell"},{"issue":"13s","key":"2349_CR147","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3582270","volume":"55","author":"X Wang","year":"2023","unstructured":"Wang X, Jin Y, Schmitt S, Olhofer M (2023) Recent advances in bayesian optimization. ACM Comput Surv 55(13s):1\u201336","journal-title":"ACM Comput Surv"},{"key":"2349_CR148","doi-asserted-by":"crossref","unstructured":"Cheng S, Leng Z, Cubuk ED, Zoph B, Bai C, Ngiam J, Song Y, Caine B, Vasudevan V, Li C et al. (2020) Improving 3d object detection through progressive population based augmentation. In: European conference on computer vision. Springer, pp 279\u2013294","DOI":"10.1007\/978-3-030-58589-1_17"},{"key":"2349_CR149","unstructured":"Cheung T-H, Yeung D-Y (2020) Modals: modality-agnostic automated data augmentation in the latent space. In: International conference on learning representations"},{"key":"2349_CR150","unstructured":"Grathwohl W, Choi D, Wu Y, Roeder G, Duvenaud D (2017) Backpropagation through the void: optimizing control variates for black-box gradient estimation. arXiv preprint arXiv:1711.00123"},{"key":"2349_CR151","unstructured":"Tucker G, Mnih A, Maddison CJ, Lawson J, Sohl-Dickstein J (2017) Rebar: low-variance, unbiased gradient estimates for discrete latent variable models. In: Advances in Neural Information Processing Systems, vol 30"},{"key":"2349_CR152","unstructured":"Zhou K, Hong L, Hu S, Zhou F, Ru B, Feng J, Li Z (2021) Dha: end-to-end joint optimization of data augmentation policy, hyper-parameter and architecture. arXiv preprint arXiv:2109.05765"},{"key":"2349_CR153","unstructured":"Wang X, Chu X, Yan J, Yang X (2021) Daas: differentiable architecture and augmentation policy search. arXiv preprint arXiv:2109.15273"},{"key":"2349_CR154","doi-asserted-by":"crossref","unstructured":"Xu J, Li M, Zhu Z (2020) Automatic data augmentation for 3d medical image segmentation. In: Medical image computing and computer assisted intervention\u2013MICCAI 2020: 23rd international conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part I 23. Springer, pp 378\u2013387","DOI":"10.1007\/978-3-030-59710-8_37"},{"key":"2349_CR155","unstructured":"Akimoto Y, Shirakawa S, Yoshinari N, Uchida K, Saito S, Nishida K (2019) Adaptive stochastic natural gradient method for one-shot neural architecture search. In: International conference on machine learning. PMLR, pp 171\u2013180"},{"key":"2349_CR156","doi-asserted-by":"crossref","unstructured":"Li Y, Hu G, Wang Y, Hospedales T, Robertson NM, Yang Y (2020) Dada: differentiable automatic data augmentation. arXiv preprint arXiv:2003.03780","DOI":"10.1007\/978-3-030-58542-6_35"},{"key":"2349_CR157","doi-asserted-by":"crossref","unstructured":"Luo Z, He Z, Wang J, Dong M, Huang J, Chen M, Zheng B (2021) Autosmart: an efficient and automatic machine learning framework for temporal relational data. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 3976\u20133984","DOI":"10.1145\/3447548.3467088"},{"key":"2349_CR158","unstructured":"Alaa A, Schaar M (2018) Autoprognosis: automated clinical prognostic modeling via bayesian optimization with structured kernel learning. In: International conference on machine learning. PMLR, pp 139\u2013148"},{"key":"2349_CR159","unstructured":"LingChen TC, Khonsari A, Lashkari A, Nazari MR, Sambee JS, Nascimento MA (2020) Uniformaugment: asearch-free probabilistic data augmentation approach. arXiv preprint arXiv:2003.14348"},{"key":"2349_CR160","unstructured":"Feurer M, Hutter F (2018) Towards further automation in automl. In: ICML AutoML workshop, vol 13"},{"key":"2349_CR161","unstructured":"Olson RS, Moore JH (2016) Tpot: a tree-based pipeline optimization tool for automating machine learning. In: Workshop on automatic machine learning. PMLR, pp 66\u201374"},{"key":"2349_CR162","unstructured":"Zhao Y (2022) Autodes: automl pipeline generation of classification with dynamic ensemble strategy selection. arXiv preprint arXiv:2201.00207"},{"key":"2349_CR163","doi-asserted-by":"crossref","unstructured":"Komer B, Bergstra J, Eliasmith C (2014) \u201cHyperopt-sklearn: automatic hyperparameter configuration for scikit-learn,\u201d in ICML workshop on AutoML, 9. Citeseer, 50","DOI":"10.25080\/Majora-14bd3278-006"},{"key":"2349_CR164","doi-asserted-by":"crossref","unstructured":"Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013) Auto-weka: Combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 847\u2013855","DOI":"10.1145\/2487575.2487629"},{"key":"2349_CR165","doi-asserted-by":"crossref","unstructured":"Swearingen T, Drevo W, Cyphers B, Cuesta-Infante A, Ross A, Veeramachaneni K (2017) Atm: a distributed, collaborative, scalable system for automated machine learning. In: 2017 IEEE international conference on big data (big data). IEEE, pp 151\u2013162","DOI":"10.1109\/BigData.2017.8257923"},{"key":"2349_CR166","unstructured":"LeDell E, Poirier S (2020) H2o automl: scalable automatic machine learning. In: Proceedings of the AutoML Workshop at ICML, p 2020"},{"key":"2349_CR167","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1016\/j.patrec.2020.08.024","volume":"138","author":"A Naghizadeh","year":"2020","unstructured":"Naghizadeh A, Abavisani M, Metaxas DN (2020) Greedy autoaugment. Pattern Recogn Lett 138:624\u2013630","journal-title":"Pattern Recogn Lett"},{"key":"2349_CR168","doi-asserted-by":"crossref","unstructured":"M\u00fcller SG, Hutter SG (2021) Trivialaugment: tuning-free yet state-of-the-art data augmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 774\u2013782","DOI":"10.1109\/ICCV48922.2021.00081"},{"issue":"2\u20133","key":"2349_CR169","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/0005-1098(63)90018-9","volume":"1","author":"DC Karnopp","year":"1963","unstructured":"Karnopp DC (1963) Random search techniques for optimization problems. Automatica 1(2\u20133):111\u2013121","journal-title":"Automatica"},{"issue":"7\u20138","key":"2349_CR170","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1177\/0278364904045481","volume":"23","author":"SM LaValle","year":"2004","unstructured":"LaValle SM, Branicky MS, Lindemann SR (2004) On the relationship between classical grid search and probabilistic roadmaps. Int J Robot Res 23(7\u20138):673\u2013692","journal-title":"Int J Robot Res"},{"key":"2349_CR171","doi-asserted-by":"crossref","unstructured":"Wilt C, Thayer J, Ruml W (2010) A comparison of y search algorithms. In: Proceedings of the international symposium on combinatorial search 1(1):129\u2013136","DOI":"10.1609\/socs.v1i1.18182"},{"key":"2349_CR172","doi-asserted-by":"publisher","first-page":"105175","DOI":"10.1016\/j.compbiomed.2021.105175","volume":"141","author":"M Momeny","year":"2022","unstructured":"Momeny M, Neshat AA, Gholizadeh A, Jafarnezhad A, Rahmanzadeh E, Marhamati M, Moradi B, Ghafoorifar A, Zhang Y-D (2022) Greedy autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy. Comput Biol Med 141:105175","journal-title":"Comput Biol Med"},{"key":"2349_CR173","unstructured":"Yao Q, Wang M, Chen Y, Dai W, Li Y-F, Tu W-W, Yang Q, Yu Y (2018) Taking human out of learning applications: a survey on automated machine learning. arXiv preprint arXiv:1810.13306"},{"key":"2349_CR174","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1613\/jair.1.11854","volume":"70","author":"M-A Z\u00f6ller","year":"2021","unstructured":"Z\u00f6ller M-A, Huber MF (2021) Benchmark and survey of automated machine learning frameworks. J Artif Intell Res 70:409\u2013472","journal-title":"J Artif Intell Res"},{"key":"2349_CR175","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database, In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2349_CR176","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona, Ramanan D, Doll\u00e1r, Zitnick CL (2014) Microsoft coco: common objects in context, In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"6","key":"2349_CR177","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/MC.2010.170","volume":"43","author":"D Anguelov","year":"2010","unstructured":"Anguelov D, Dulong C, Filip D, Frueh C, Lafon S, Lyon R, Ogale A, Vincent L, Weaver J (2010) Google street view: capturing the world at street level. Computer 43(6):32\u201338","journal-title":"Computer"},{"key":"2349_CR178","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115:211\u2013252","journal-title":"Int J Comput Vision"},{"key":"2349_CR179","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2016) Wide residual networks. arXiv preprint arXiv:1605.07146","DOI":"10.5244\/C.30.87"},{"key":"2349_CR180","unstructured":"Gastaldi X (2017) Shake-shake regularization. arXiv preprint arXiv:1705.07485"},{"key":"2349_CR181","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"2349_CR182","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks, In: Computer vision\u2013ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV 14"},{"key":"2349_CR183","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Girshick Goyal R, He K, Doll\u00e1r (2017) Focal loss for dense object detection, In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"2349_CR184","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2349_CR185","unstructured":"Chen, Liu S, Zhao H, Jia J (2020) Gridmask data augmentation. arXiv preprint arXiv:2001.04086"},{"key":"2349_CR186","doi-asserted-by":"crossref","unstructured":"Zhou F, Li J,Xie C, Chen F, Hong L, Sun R, Li Z (2020) Metaaugment: sample-aware data augmentation policy learning. arXiv preprint arXiv:2012.12076","DOI":"10.1609\/aaai.v35i12.17324"},{"key":"2349_CR187","doi-asserted-by":"crossref","unstructured":"Dong X, Potter M, Kumar G, Tsai Y-C, Saripalli VR (2021) Automating augmentation through random unidimensional search. arXiv preprint arXiv:2106.08756","DOI":"10.1007\/978-3-031-24866-5_23"},{"key":"2349_CR188","doi-asserted-by":"crossref","unstructured":"Suzuki T (2022) Teachaugment: data augmentation optimization using teacher knowledge. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10904\u201310914","DOI":"10.1109\/CVPR52688.2022.01063"},{"issue":"11","key":"2349_CR189","first-page":"9851","volume":"35","author":"A Terauchi","year":"2021","unstructured":"Terauchi A, Mori N (2021) Evolutionary approach for autoaugment using the thermodynamical genetic algorithm. Proc AAAI Conf Artif Intell 35(11):9851\u20139858","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"2349_CR190","doi-asserted-by":"crossref","unstructured":"Dabouei A, Soleymani S, Taherkhani F, Nasrabadi NM (2021) Supermix: supervising the mixing data augmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13794\u201313803","DOI":"10.1109\/CVPR46437.2021.01358"},{"key":"2349_CR191","doi-asserted-by":"crossref","unstructured":"Huang G, Sun Y, Liu Z, Sedra D, Weinberger KQ (2016) Deep networks with stochastic depth. In: European conference on computer vision. Springer, pp 646\u2013661","DOI":"10.1007\/978-3-319-46493-0_39"},{"key":"2349_CR192","unstructured":"Wang Y, Pan X, Song S, Zhang H, Huang G, Wu C (2019) Implicit semantic data augmentation for deep networks. In: Advances in neural information processing systems, vol 32"},{"key":"2349_CR193","unstructured":"Verma V, Lamb A, Beckham C, Najafi A, Mitliagkas I, Lopez-Paz D, Bengio Y (2019) Manifold mixup: Better representations by interpolating hidden states. In: International conference on machine learning. PMLR, 2019, pp 6438\u20136447"},{"key":"2349_CR194","unstructured":"Kim J-H, Choo W, Song HO (2020) Puzzle mix: exploiting saliency and local statistics for optimal mixup. In: International conference on machine learning. PMLR, pp 5275\u20135285"},{"key":"2349_CR195","first-page":"689","volume":"33","author":"A Khan","year":"2020","unstructured":"Khan A, Fraz K (2020) Post-training iterative hierarchical data augmentation for deep networks. Adv Neural Inf Process Syst 33:689\u2013699","journal-title":"Adv Neural Inf Process Syst"},{"key":"2349_CR196","unstructured":"Uddin A, Monira M, Shin W, Chung T, Bae S-H et al. (2020) Saliencymix: a saliency guided data augmentation strategy for better regularization. arXiv preprint arXiv:2006.01791"},{"key":"2349_CR197","doi-asserted-by":"crossref","unstructured":"Atienza R (2022) Improving model generalization by agreement of learned representations from data augmentation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 372\u2013381","DOI":"10.1109\/WACV51458.2022.00398"},{"issue":"07","key":"2349_CR198","first-page":"13001","volume":"34","author":"Z Zhong","year":"2020","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. Proc AAAI Conf Artif Intell 34(07):13001\u201313008","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"2349_CR199","doi-asserted-by":"crossref","unstructured":"Walawalkar D, Shen Z, Liu Z, Savvides M (2020) Attentive cutmix: an enhanced data augmentation approach for deep learning based image classification. arXiv preprint arXiv:2003.13048","DOI":"10.1109\/ICASSP40776.2020.9053994"},{"key":"2349_CR200","unstructured":"Wen Y, Jerfel G, Muller R, Dusenberry MW, Snoek J, Lakshminarayanan B, Tran D (2020) Combining ensembles and data augmentation can harm your calibration. arXiv preprint arXiv:2010.09875"},{"key":"2349_CR201","doi-asserted-by":"crossref","unstructured":"Gudovskiy D, Rigazio L, Ishizaka S, Kozuka K, Tsukizawa S (2021) Autodo: robust autoaugment for biased data with label noise via scalable probabilistic implicit differentiation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 16601\u201316610","DOI":"10.1109\/CVPR46437.2021.01633"},{"issue":"1","key":"2349_CR202","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41698-021-00245-5","volume":"6","author":"I Tsamardinos","year":"2022","unstructured":"Tsamardinos I, Charonyktakis Papoutsoglou G, Borboudakis G, Lakiotaki K, Zenklusen JC, Juhl H, Chatzaki E, Lagani V (2022) Just add data: automated predictive modeling for knowledge discovery and feature selection. NPJ Precis Oncol 6(1):1\u201317","journal-title":"NPJ Precis Oncol"},{"key":"2349_CR203","unstructured":"Xanthopoulos I, Tsamardinos I, Christophides V, Simon E, Salinger A (2020) Putting the human back in the automl loop. In: EDBT\/ICDT Workshops"},{"key":"2349_CR204","doi-asserted-by":"crossref","unstructured":"Yang Z, Zeng W, Jin S, Qian C, Luo, Liu W (2024) Autommlab: automatically generating deployable models from language instructions for computer vision tasks. arXiv preprint arXiv:2402.15351","DOI":"10.1609\/aaai.v39i21.34358"},{"key":"2349_CR205","unstructured":"Cao K, You J, Liu J, Leskovec J (2023) Autotransfer: Automl with knowledge transfer\u2013an application to graph neural networks. arXiv preprint arXiv:2303.07669"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02349-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-025-02349-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02349-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T05:04:21Z","timestamp":1744434261000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-025-02349-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,22]]},"references-count":205,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["2349"],"URL":"https:\/\/doi.org\/10.1007\/s10115-025-02349-x","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,22]]},"assertion":[{"value":"11 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}