{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T12:46:21Z","timestamp":1770295581059,"version":"3.49.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000181","name":"US Air Force Office of Scientific Research","doi-asserted-by":"crossref","award":["FA8655-20-1-7037"],"award-info":[{"award-number":["FA8655-20-1-7037"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000181","name":"US Air Force Office of Scientific Research","doi-asserted-by":"crossref","award":["FA8655-20-1-7037"],"award-info":[{"award-number":["FA8655-20-1-7037"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000181","name":"US Air Force Office of Scientific Research","doi-asserted-by":"crossref","award":["FA8655-20-1-7037"],"award-info":[{"award-number":["FA8655-20-1-7037"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"crossref"}]},{"name":"University SAL Labs"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Mitigating catastrophic forgetting in continual learning is a long-standing challenge for artificial intelligence. Often, methods used to alleviate forgetting make use of either rehearsal buffers, pretrained backbones or task-id knowledge. However, these requirements result in severe limitations regarding scalability, privacy preservation, and efficient deployment. In this work, we explore how to eliminate the need for such requirements in incremental learning approaches based on parameter isolation. We propose Low Interference Feature Extraction Subnetworks (LIFES), a method that learns a subnetwork per task and uses all of them concurrently at inference time. This solution minimises requirements; however, it creates the need to address certain challenges. To formalize them, we break down the catastrophic forgetting problem into 4 distinct causes, and address them with a novel lateral classifiers regularization, weight standardization, and subnetwork interference connection pruning. Specifically, the use of lateral classification shows very promising results, forcing the model to learn distributions with higher inter-class distance. Using these components, LIFES achieves competitive results in standard task-agnostic scenarios, demonstrating the viability of this new perspective for parameter isolation, which has minimal requirements. Finally, we discuss how future work can improve this new paradigm further, and how the strategies defined can be complementary to other approaches.<\/jats:p>","DOI":"10.1007\/s11063-025-11792-4","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T10:02:39Z","timestamp":1756720959000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["From task-aware to task-agnostic parameter isolation for incremental learning"],"prefix":"10.1007","volume":"57","author":[{"given":"Alex","family":"Vicente-Sola","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Kirkland","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaetano","family":"Di Caterina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trevor J","family":"Bihl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Masana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"issue":"7553","key":"11792_CR1","first-page":"436","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning nature 521(7553):436\u2013444","journal-title":"Deep learning nature"},{"key":"11792_CR2","unstructured":"Verwimp E, Ben-David S, Bethge M, Cossu A, Gepperth A, Hayes TL, H\u00fcllermeier E, Kanan C, Kudithipudi D, Lampert CH et al (2023) Continual learning: Applications and the road forward. arXiv preprint arXiv:2311.11908"},{"issue":"4","key":"11792_CR3","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","volume":"3","author":"RM French","year":"1999","unstructured":"French RM (1999) Catastrophic forgetting in connectionist networks. Trends Cogn Sci 3(4):128\u2013135","journal-title":"Trends Cogn Sci"},{"key":"11792_CR4","doi-asserted-by":"crossref","unstructured":"Kemker R, McClure M, Abitino A, Hayes T, Kanan C (2018) Measuring catastrophic forgetting in neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32","DOI":"10.1609\/aaai.v32i1.11651"},{"issue":"7","key":"11792_CR5","first-page":"3366","volume":"44","author":"M De Lange","year":"2021","unstructured":"De Lange M, Aljundi R, Masana M, Parisot S, Jia X, Leonardis A, Slabaugh G, Tuytelaars T (2021) A continual learning survey: Defying forgetting in classification tasks. Transactions on Pattern Analysis and Machine Intelligence 44(7):3366\u20133385","journal-title":"Transactions on Pattern Analysis and Machine Intelligence"},{"key":"11792_CR6","doi-asserted-by":"publisher","first-page":"54654","DOI":"10.3389\/fpsyg.2013.00504","volume":"4","author":"M Mermillod","year":"2013","unstructured":"Mermillod M, Bugaiska A, Bonin P (2013) The stability-plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects. Front Psychol 4:54654","journal-title":"Front Psychol"},{"issue":"5","key":"11792_CR7","doi-asserted-by":"publisher","first-page":"5513","DOI":"10.1109\/TPAMI.2022.3213473","volume":"45","author":"M Masana","year":"2023","unstructured":"Masana M, Liu X, Twardowski B, Menta M, Bagdanov AD, Weijer J (2023) Class-incremental learning: Survey and performance evaluation on image classification. IEEE Trans Pattern Anal Mach Intell 45(5):5513\u20135533. https:\/\/doi.org\/10.1109\/TPAMI.2022.3213473","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11792_CR8","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.neunet.2020.12.003","volume":"135","author":"E Belouadah","year":"2021","unstructured":"Belouadah E, Popescu A, Kanellos I (2021) A comprehensive study of class incremental learning algorithms for visual tasks. Neural Netw 135:38\u201354","journal-title":"Neural Netw"},{"key":"11792_CR9","doi-asserted-by":"crossref","unstructured":"Wang L, Zhang X, Su H, Zhu J (2024) A comprehensive survey of continual learning: theory, method and application. IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109\/TPAMI.2024.3367329"},{"key":"11792_CR10","unstructured":"Rusu AA, Rabinowitz NC, Desjardins G, Soyer H, Kirkpatrick J, Kavukcuoglu K, Pascanu R, Hadsell R (2016) Progressive neural networks. arXiv preprint arXiv:1606.04671"},{"key":"11792_CR11","unstructured":"Serra J, Suris D, Miron M, Karatzoglou A (2018) Overcoming catastrophic forgetting with hard attention to the task. In: International Conference on Machine Learning, pp. 4548\u20134557. PMLR"},{"key":"11792_CR12","unstructured":"Kang H, Mina RJL, Madjid SRH, Yoon J, Hasegawa-Johnson M, Hwang SJ, Yoo CD (2022) Forget-free continual learning with winning subnetworks. In: International Conference on Machine Learning, pp. 10734\u201310750. PMLR"},{"issue":"13","key":"11792_CR13","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, Milan K, Quan J, Ramalho T, Grabska-Barwinska A et al (2017) Overcoming catastrophic forgetting in neural networks. Proc Natl Acad Sci 114(13):3521\u20133526","journal-title":"Proc Natl Acad Sci"},{"issue":"12","key":"11792_CR14","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li Z, Hoiem D (2017) Learning without forgetting. IEEE Trans Pattern Anal Mach Intell 40(12):2935\u20132947","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11792_CR15","doi-asserted-by":"crossref","unstructured":"Petit G, Popescu A, Schindler H, Picard D, Delezoide B (2023) Fetril: Feature translation for exemplar-free class-incremental learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3911\u20133920","DOI":"10.1109\/WACV56688.2023.00390"},{"key":"11792_CR16","unstructured":"Krizhevsky A, Hinton G, et al (2009) Learning multiple layers of features from tiny images. Master\u2019s thesis, University of Toronto"},{"key":"11792_CR17","unstructured":"Le Y, Yang X (2015) Tiny imagenet visual recognition challenge. CS 231N 7(7), 3"},{"key":"11792_CR18","doi-asserted-by":"crossref","unstructured":"Cao Y, Yang J (2015) Towards making systems forget with machine unlearning. In: 2015 IEEE Symposium on Security and Privacy, pp. 463\u2013480. IEEE","DOI":"10.1109\/SP.2015.35"},{"key":"11792_CR19","doi-asserted-by":"crossref","unstructured":"Dang Q-V (2021) Right to be forgotten in the age of machine learning. In: Advances in Digital Science: ICADS 2021, pp. 403\u2013411. Springer","DOI":"10.1007\/978-3-030-71782-7_35"},{"key":"11792_CR20","doi-asserted-by":"crossref","unstructured":"Aljundi R, Babiloni F, Elhoseiny M, Rohrbach M, Tuytelaars T (2018) Memory aware synapses: Learning what (not) to forget. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 139\u2013154","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"11792_CR21","unstructured":"Jung H, Ju J, Jung M, Kim J (2016) Less-forgetting learning in deep neural networks. arXiv preprint arXiv:1607.00122"},{"key":"11792_CR22","doi-asserted-by":"crossref","unstructured":"Rebuffi S-A, Kolesnikov A, Sperl G, Lampert CH (2017) icarl: Incremental classifier and representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2001\u20132010","DOI":"10.1109\/CVPR.2017.587"},{"key":"11792_CR23","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhang J, Ghosh S, Li D, Tasci S, Heck L, Zhang H, Kuo C-CJ (2020) Class-incremental learning via deep model consolidation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1131\u20131140","DOI":"10.1109\/WACV45572.2020.9093365"},{"key":"11792_CR24","doi-asserted-by":"crossref","unstructured":"Zhu F, Zhang X-Y, Wang C, Yin F, Liu C-L (2021) Prototype augmentation and self-supervision for incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5871\u20135880","DOI":"10.1109\/CVPR46437.2021.00581"},{"key":"11792_CR25","doi-asserted-by":"crossref","unstructured":"Zhu K, Zhai W, Cao Y, Luo J, Zha Z-J (2022) Self-sustaining representation expansion for non-exemplar class-incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9296\u20139305","DOI":"10.1109\/CVPR52688.2022.00908"},{"key":"11792_CR26","first-page":"14306","volume":"34","author":"Class-incremental learning via dual augmentation","year":"2021","unstructured":"Class-incremental learning via dual augmentation (2021) Zhu, F., Cheng, Z., Zhang, X.-Y., Liu, C.-l. Adv Neural Inf Process Syst 34:14306\u201314318","journal-title":"Adv Neural Inf Process Syst"},{"key":"11792_CR27","doi-asserted-by":"crossref","unstructured":"Shi W, Ye M (2023) Prototype reminiscence and augmented asymmetric knowledge aggregation for non-exemplar class-incremental learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1772\u20131781","DOI":"10.1109\/ICCV51070.2023.00170"},{"key":"11792_CR28","unstructured":"Yoon J, Yang E, Lee J, Hwang SJ (2017) Lifelong learning with dynamically expandable networks. arXiv preprint arXiv:1708.01547"},{"key":"11792_CR29","first-page":"14150","volume":"34","author":"J Hurtado","year":"2021","unstructured":"Hurtado J, Raymond A, Soto A (2021) Optimizing reusable knowledge for continual learning via metalearning. Adv Neural Inf Process Syst 34:14150\u201314162","journal-title":"Adv Neural Inf Process Syst"},{"key":"11792_CR30","doi-asserted-by":"crossref","unstructured":"Yan S, Xie J, He X (2021) Der: Dynamically expandable representation for class incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3014\u20133023","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"11792_CR31","unstructured":"Zhou D-W, Wang Q-W, Ye H-J, Zhan D-C (2022) A model or 603 exemplars: Towards memory-efficient class-incremental learning. arXiv preprint arXiv:2205.13218"},{"key":"11792_CR32","doi-asserted-by":"crossref","unstructured":"Mallya A, Lazebnik S (2018) Packnet: Adding multiple tasks to a single network by iterative pruning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7765\u20137773","DOI":"10.1109\/CVPR.2018.00810"},{"key":"11792_CR33","doi-asserted-by":"crossref","unstructured":"Masana M, Tuytelaars T, Weijer J (2021) Ternary feature masks: zero-forgetting for task-incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3570\u20133579","DOI":"10.1109\/CVPRW53098.2021.00396"},{"key":"11792_CR34","unstructured":"Kang H, Mina RJL, Madjid SRH, Yoon, J, Hasegawa-Johnson M, Hwang SJ, Yoo CD (2022) Forget-free continual learning with winning subnetworks. In: Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. (eds.) Proceedings of the 39th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 162, pp. 10734\u201310750. PMLR, ???"},{"key":"11792_CR35","unstructured":"Kim G, Liu B, Ke Z (2022) A multi-head model for continual learning via out-of-distribution replay. In: Conference on Lifelong Learning Agents, pp. 548\u2013563. PMLR"},{"key":"11792_CR36","unstructured":"Kim G, Xiao C, Konishi T, Liu B (2023) Learnability and algorithm for continual learning. In: International Conference on Machine Learning, pp. 16877\u201316896. PMLR"},{"key":"11792_CR37","unstructured":"Wortsman M, Ramanujan V, Liu R, Kembhavi A, Rastegari M, Yosinski J, Farhadi A (2020) Supermasks in superposition. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 15173\u201315184. Curran Associates, Inc., ???"},{"key":"11792_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2021.01.078","volume":"439","author":"G Sokar","year":"2021","unstructured":"Sokar G, Mocanu DC, Pechenizkiy M (2021) Spacenet: Make free space for continual learning. Neurocomputing 439:1\u201311","journal-title":"Neurocomputing"},{"key":"11792_CR39","unstructured":"Kaushik P, Gain A, Kortylewski A, Yuille A (2021) Understanding catastrophic forgetting and remembering in continual learning with optimal relevance mapping. arXiv preprint arXiv:2102.11343"},{"key":"11792_CR40","doi-asserted-by":"crossref","unstructured":"Bonato J, Pelosin F, Sabetta L, Nicolosi A (2024) Mind: Multi-task incremental network distillation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 11105\u201311113","DOI":"10.1609\/aaai.v38i10.28987"},{"key":"11792_CR41","doi-asserted-by":"crossref","unstructured":"Castro FM, Mar\u00edn-Jim\u00e9nez MJ, Guil N, Schmid C, Alahari K (2018) End-to-end incremental learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 233\u2013248","DOI":"10.1007\/978-3-030-01258-8_15"},{"key":"11792_CR42","doi-asserted-by":"crossref","unstructured":"Wu Y, Chen Y, Wang L, Ye Y, Liu Z, Guo Y, Fu Y (2019) Large scale incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 374\u2013382","DOI":"10.1109\/CVPR.2019.00046"},{"key":"11792_CR43","doi-asserted-by":"crossref","unstructured":"Hou S, Pan X, Loy CC, Wang Z, Lin D (2019) Learning a unified classifier incrementally via rebalancing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 831\u2013839","DOI":"10.1109\/CVPR.2019.00092"},{"key":"11792_CR44","doi-asserted-by":"crossref","unstructured":"Belouadah E, Popescu A (2019) Il2m: Class incremental learning with dual memory. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 583\u2013592","DOI":"10.1109\/ICCV.2019.00067"},{"key":"11792_CR45","doi-asserted-by":"crossref","unstructured":"Zhao B, Xiao X, Gan G, Zhang B, Xia S-T (2020) Maintaining discrimination and fairness in class incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13208\u201313217","DOI":"10.1109\/CVPR42600.2020.01322"},{"key":"11792_CR46","doi-asserted-by":"crossref","unstructured":"Mallya A, Davis D, Lazebnik S (2018) Piggyback: Adapting a single network to multiple tasks by learning to mask weights. In: Proceedings of the European Conference on Computer Vision, pp. 67\u201382","DOI":"10.1007\/978-3-030-01225-0_5"},{"key":"11792_CR47","doi-asserted-by":"crossref","unstructured":"Jin H, Kim E (2022) Helpful or harmful: Inter-task association in continual learning. In: European Conference on Computer Vision, pp. 519\u2013535. Springer","DOI":"10.1007\/978-3-031-20083-0_31"},{"key":"11792_CR48","unstructured":"Qiao S, Wang H, Liu C, Shen W, Yuille A (2019) Micro-batch training with batch-channel normalization and weight standardization. arXiv preprint arXiv:1903.10520"},{"key":"11792_CR49","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. pmlr"},{"key":"11792_CR50","unstructured":"Brock A, De S, Smith SL (2021) Characterizing signal propagation to close the performance gap in unnormalized resnets. In: International Conference on Learning Representations"},{"key":"11792_CR51","doi-asserted-by":"crossref","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13, pp. 818\u2013833. Springer","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"11792_CR52","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? Advances in neural information processing systems 27"},{"key":"11792_CR53","unstructured":"Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2014) Decaf: A deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647\u2013655. PMLR"},{"key":"11792_CR54","unstructured":"Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M (2014) Striving for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806"},{"key":"11792_CR55","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":"11792_CR56","doi-asserted-by":"crossref","unstructured":"Chaudhry A, Dokania PK, Ajanthan T, Torr PH (2018) Riemannian walk for incremental learning: Understanding forgetting and intransigence. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 532\u2013547","DOI":"10.1007\/978-3-030-01252-6_33"},{"issue":"6","key":"11792_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3538531","volume":"55","author":"D Kleyko","year":"2022","unstructured":"Kleyko D, Rachkovskij DA, Osipov E, Rahimi A (2022) A survey on hyperdimensional computing aka vector symbolic architectures, part i: Models and data transformations. ACM Comput Surv 55(6):1\u201340","journal-title":"ACM Comput Surv"},{"key":"11792_CR58","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"11792_CR59","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR"},{"issue":"4","key":"11792_CR60","doi-asserted-by":"publisher","DOI":"10.1088\/2634-4386\/ac8bef","volume":"2","author":"A Vicente-Sola","year":"2022","unstructured":"Vicente-Sola A, Manna DL, Kirkland P, Di Caterina G, Bihl T (2022) Keys to accurate feature extraction using residual spiking neural networks. Neuromorphic Computing and Engineering 2(4):044001","journal-title":"Neuromorphic Computing and Engineering"},{"key":"11792_CR61","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30"},{"key":"11792_CR62","unstructured":"Brock A, De S, Smith SL, Simonyan K (2021) High-performance large-scale image recognition without normalization. In: International Conference on Machine Learning, pp. 1059\u20131071. PMLR"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-025-11792-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-025-11792-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-025-11792-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T04:43:57Z","timestamp":1760762637000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-025-11792-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"references-count":62,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["11792"],"URL":"https:\/\/doi.org\/10.1007\/s11063-025-11792-4","relation":{},"ISSN":["1573-773X"],"issn-type":[{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,1]]},"assertion":[{"value":"13 July 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2025","order":2,"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 that there are no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"81"}}