{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:13:10Z","timestamp":1777655590701,"version":"3.51.4"},"reference-count":79,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Science and Technology Innovation (STI) 2030-Major Projects of China","award":["2021ZD0201300"],"award-info":[{"award-number":["2021ZD0201300"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2024300394"],"award-info":[{"award-number":["2024300394"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276127"],"award-info":[{"award-number":["62276127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tip.2025.3592538","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T18:53:14Z","timestamp":1753901594000},"page":"4843-4855","source":"Crossref","is-referenced-by-count":6,"title":["AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation"],"prefix":"10.1109","volume":"34","author":[{"given":"Suorong","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3739-9825","authenticated-orcid":false,"given":"Peijia","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology and the School of Artificial Intelligence, Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Xiong","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology and the School of Artificial Intelligence, Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7285-326X","authenticated-orcid":false,"given":"Furao","family":"Shen","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology and the School of Artificial Intelligence, Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3949-352X","authenticated-orcid":false,"given":"Jian","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3152245"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3115672"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3175429"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3282258"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3377439"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109347"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"ref8","article-title":"Image data augmentation for deep learning: A survey","author":"Yang","year":"2022","journal-title":"arXiv:2204.08610"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110204"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3128401"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3364500"},{"key":"ref12","article-title":"When dynamic data selection meets data augmentation","author":"Yang","year":"2025","journal-title":"arXiv:2505.03809"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2913544"},{"key":"ref14","article-title":"Improved regularization of convolutional neural networks with cutout","author":"DeVries","year":"2017","journal-title":"arXiv:1708.04552"},{"key":"ref15","article-title":"GridMask data augmentation","author":"Chen","year":"2020","journal-title":"arXiv:2001.04086"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109847"},{"key":"ref17","first-page":"1","article-title":"Mixup: Beyond empirical risk minimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3336532"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3049011"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00081"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i2.25247"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01063"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2016.7533048"},{"key":"ref27","first-page":"1","article-title":"Adaaug: Learning class-and instance-adaptive data augmentation policies","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Cheung"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58542-6_35"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01611-x"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00111"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-64984-5_27"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CDS52072.2021.00107"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3402543"},{"key":"ref34","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref35","article-title":"A downsampled variant of ImageNet as an alternative to the CIFAR datasets","author":"Chrabaszcz","year":"2017","journal-title":"arXiv:1707.08819"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00264"},{"key":"ref38","article-title":"Collecting a large-scale dataset of fine-grained cars","author":"Krause","year":"2013"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"ref41","article-title":"Fine-grained visual classification of aircraft","author":"Maji","year":"2013","journal-title":"arXiv:1306.5151"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i1.25191"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72848-8_12"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.381"},{"key":"ref46","first-page":"6665","article-title":"Fast AutoAugment","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Lim"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58595-2_1"},{"key":"ref48","article-title":"Adversarial AutoAugment","author":"Zhang","year":"2019","journal-title":"arXiv:1912.11188"},{"key":"ref49","first-page":"5705","article-title":"Meta-learning requires meta-augmentation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Rajendran"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00165"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01097"},{"key":"ref52","article-title":"Universal adaptive data augmentation","author":"Xu","year":"2022","journal-title":"arXiv:2207.06658"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10095350"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10061-9"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-7784-8_10"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3054625"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3280161"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2023.0140838"},{"key":"ref59","first-page":"1057","article-title":"Policy gradient methods for reinforcement learning with function approximation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"12","author":"Sutton"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120495"},{"key":"ref61","first-page":"1861","article-title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Haarnoja"},{"key":"ref62","article-title":"Continuous control with deep reinforcement learning","author":"Lillicrap","year":"2015","journal-title":"arXiv:1509.02971"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2012.2218595"},{"key":"ref64","first-page":"1928","article-title":"Asynchronous methods for deep reinforcement learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mnih"},{"key":"ref65","article-title":"Feature map testing for deep neural networks","author":"Huang","year":"2023","journal-title":"arXiv:2307.11563"},{"key":"ref66","article-title":"A-FMI: Learning attributions from deep networks via feature map importance","author":"Zhang","year":"2021","journal-title":"arXiv:2104.05527"},{"key":"ref67","first-page":"5124","article-title":"Data-efficient augmentation for training neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref68","first-page":"1361","article-title":"CLUTR: Curriculum learning via unsupervised task representation learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Azad"},{"issue":"7","key":"ref69","first-page":"1","article-title":"Convolutional deep belief networks on cifar-10","volume":"40","author":"Krizhevsky","year":"2010","journal-title":"Unpublished Manuscript"},{"key":"ref70","article-title":"Asynchronous methods for deep reinforcement learning","author":"Mnih","year":"2016","journal-title":"arXiv:1602.01783"},{"key":"ref71","article-title":"Robust opponent modeling via adversarial ensemble reinforcement learning in asymmetric imperfect-information games","author":"Shen","year":"2019","journal-title":"arXiv:1909.08735"},{"key":"ref72","article-title":"Meta reinforcement learning as task inference","author":"Humplik","year":"2019","journal-title":"arXiv:1905.06424"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.5244\/c.30.87"},{"key":"ref75","article-title":"Shake-shake regularization","author":"Gastaldi","year":"2017","journal-title":"arXiv:1705.07485"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3004555"},{"issue":"86","key":"ref78","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1016\/j.parco.2021.102751"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/83\/10795784\/11104992.pdf?arnumber=11104992","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T18:08:49Z","timestamp":1754417329000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11104992\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":79,"URL":"https:\/\/doi.org\/10.1109\/tip.2025.3592538","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}