{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T05:32:09Z","timestamp":1767677529847,"version":"3.48.0"},"reference-count":80,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/Crown.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1109\/tai.2025.3570282","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T13:36:10Z","timestamp":1747316170000},"page":"571-585","source":"Crossref","is-referenced-by-count":3,"title":["Reduction of Class Activation Uncertainty With Background Information"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3395-1772","authenticated-orcid":false,"given":"H. M. Dipu","family":"Kabir","sequence":"first","affiliation":[{"name":"Artificial Intelligence and Cyber Futures Institute and the Rural Health Research Institute, Charles Sturt University, Bathurst, NSW, Australia"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"13","author":"Caruana","year":"2000"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58558-7_29"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00540"},{"article-title":"An evolutionary approach to dynamic introduction of tasks in large-scale multitask learning systems","year":"2022","author":"Gesmundo","key":"ref6"},{"article-title":"Flexible parameter sharing for multi-task learning","year":"2021","author":"Kokiopoulou","key":"ref7"},{"key":"ref8","first-page":"9120","article-title":"Which tasks should be learned together in multi-task learning?","volume-title":"Proc. Int. Conf. on Mach. Learn.","author":"Standley","year":"2020"},{"article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","year":"2020","author":"Dosovitskiy","key":"ref9"},{"key":"ref10","first-page":"499","article-title":"Stability and generalization","volume":"2","author":"Bousquet","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/SPICSCON64195.2024.10940743"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1023\/A:1012450327387"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0711-5"},{"issue":"7","key":"ref14","first-page":"1099","article-title":"Learning the kernel function via regularization","volume":"6","author":"Micchelli","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118736"},{"key":"ref17","first-page":"4095","article-title":"Efficient neural architecture search via parameters sharing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pham","year":"2018"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045502"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.2307\/2288636"},{"key":"ref20","first-page":"231","article-title":"Neural network ensembles, cross validation, and active learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"7","author":"Krogh","year":"1994"},{"key":"ref21","first-page":"1","article-title":"Generalization in multitask deep neural classifiers: A statistical physics approach","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Ndirango","year":"2019"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.293"},{"key":"ref23","first-page":"109","article-title":"Scaling & shifting your features: A new baseline for efficient model tuning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Lian","year":"2022"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.26047"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00873"},{"article-title":"Coursera: Machine learning","year":"2020","author":"Ng","key":"ref26"},{"article-title":"Ablation programming for machine learning","year":"2019","author":"Sheikholeslami","key":"ref27"},{"key":"ref28","first-page":"1","article-title":"On the importance of single directions for generalization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Morcos","year":"2018"},{"key":"ref29","first-page":"983","article-title":"Ablation-cam: Visual explanations for deep convolutional network via gradient-free localization","volume-title":"Proc. IEEE\/CVF Winter Conf. Appl. Comput. Vis.","author":"Ramaswamy","year":"2020"},{"article-title":"Revisiting the importance of individual units in CNNs via ablation","year":"2018","author":"Zhou","key":"ref30"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CP.1943-5487.0000141"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s40684-016-0039-x"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/978-1-62703-748-8_7","article-title":"Introduction to machine learning","volume-title":"miRNomics: MicroRNA Biology and Computational Analysis","author":"Ba\u015ftanlar","year":"2014"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2009.05.004"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2004.383"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00039"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3178128"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3195549"},{"key":"ref41","first-page":"1","article-title":"How transferable are features in deep neural networks?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"27","author":"Yosinski","year":"2014"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/1873951.1874254"},{"article-title":"ResNet strikes back: An improved training procedure in timm","year":"2021","author":"Wightman","key":"ref43"},{"volume-title":"Deep Learning","year":"2016","author":"Goodfellow","key":"ref44"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00494"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_36"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0057"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ-IEEE.2016.7737705"},{"key":"ref49","first-page":"7482","article-title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Kendall","year":"2018"},{"key":"ref50","first-page":"4075","article-title":"High-quality prediction intervals for deep learning: A distribution-free, ensembled approach","volume-title":"Proc. Int. Conf. Mach. Learn","author":"Pearce","year":"2018"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106878"},{"key":"ref52","first-page":"215","article-title":"An analysis of single-layer networks in unsupervised feature learning","volume-title":"Proc. 14th Int. Conf. Artif. Intell. Statist. JMLR Workshop Conf. Proc.","author":"Coates","year":"2011"},{"volume-title":"The Caltech-UCSD Birds-200-2011 Dataset","year":"2011","author":"Wah","key":"ref53"},{"article-title":"Fine-grained visual classification of aircraft","year":"2013","author":"Maji","key":"ref54"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111908"},{"article-title":"Deep learning for classical Japanese literature","year":"2018","author":"Clanuwat","key":"ref57"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00675"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2022.3185179"},{"key":"ref62","first-page":"1","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Hendrycks","year":"2018"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.11.005"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2023.103800"},{"key":"ref66","first-page":"1","article-title":"AttCAT: Explaining transformers via attentive class activation tokens","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Qiang","year":"2022"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3324497"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3282951"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2023.113856"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3362475"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.59717\/j.xinn-geo.2024.100055"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122901"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123559"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102830"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123585"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489153"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00381"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2878958"},{"article-title":"Explaining and harnessing adversarial examples","year":"2014","author":"Goodfellow","key":"ref80"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9078688\/11329125\/11004632.pdf?arnumber=11004632","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T05:27:49Z","timestamp":1767677269000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11004632\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":80,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tai.2025.3570282","relation":{},"ISSN":["2691-4581"],"issn-type":[{"type":"electronic","value":"2691-4581"}],"subject":[],"published":{"date-parts":[[2026,1]]}}}