{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T10:04:07Z","timestamp":1770717847570,"version":"3.49.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"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":["J Supercomput"],"DOI":"10.1007\/s11227-026-08270-6","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T13:56:43Z","timestamp":1770645403000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sparsity allocation and channel importance selection through dual-action agent driven and weight reconstruction"],"prefix":"10.1007","volume":"82","author":[{"given":"Rui","family":"Cai","sequence":"first","affiliation":[]},{"given":"Yixin","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"8270_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110671","volume":"155","author":"A Jayasimhan","year":"2024","unstructured":"Jayasimhan A, Pabitha P (2024) Resprune: an energy-efficient restorative filter pruning method using stochastic optimization for accelerating cnn. Pattern Recognit 155:110671","journal-title":"Pattern Recognit"},{"key":"8270_CR2","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531"},{"key":"8270_CR3","doi-asserted-by":"crossref","unstructured":"Rastegari M, Ordonez V, Redmon J, Farhadi A (2016) Xnor-net: Imagenet classification using binary convolutional neural networks. In: European Conference on Computer Vision, pp. 525\u2013542. Springer","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"8270_CR4","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"8270_CR5","doi-asserted-by":"crossref","unstructured":"Singh P, Verma VK, Rai P, Namboodiri V (2020) Leveraging filter correlations for deep model compression. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 835\u2013844","DOI":"10.1109\/WACV45572.2020.9093331"},{"key":"8270_CR6","doi-asserted-by":"crossref","unstructured":"Ganjdanesh A, Gao S, Huang H (2024) Jointly training and pruning cnns via learnable agent guidance and alignment. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16058\u201316069","DOI":"10.1109\/CVPR52733.2024.01520"},{"key":"8270_CR7","doi-asserted-by":"crossref","unstructured":"Carreira-Perpinan MA, Idelbayev Y (2018) Learning-compression algorithms for neural net pruning. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8532\u20138541","DOI":"10.1109\/CVPR.2018.00890"},{"key":"8270_CR8","unstructured":"Lee N, Ajanthan T, Gould S, Torr PH (2019) A signal propagation perspective for pruning neural networks at initialization. arXiv preprint arXiv:1906.06307"},{"key":"8270_CR9","unstructured":"Filters\u2019Importance D (2016) Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710"},{"key":"8270_CR10","doi-asserted-by":"crossref","unstructured":"Molchanov P, Mallya A, Tyree S, Frosio I, Kautz J (2019) Importance estimation for neural network pruning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11264\u201311272","DOI":"10.1109\/CVPR.2019.01152"},{"key":"8270_CR11","doi-asserted-by":"crossref","unstructured":"He Y, Liu P, Wang Z, Hu Z, Yang Y (2019) Filter pruning via geometric median for deep convolutional neural networks acceleration. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4335\u20134344","DOI":"10.1109\/CVPR.2019.00447"},{"key":"8270_CR12","doi-asserted-by":"crossref","unstructured":"He Y, Kang G, Dong X, Fu Y, Yang Y (2018) Soft filter pruning for accelerating deep convolutional neural networks. arXiv preprint arXiv:1808.06866","DOI":"10.24963\/ijcai.2018\/309"},{"issue":"5","key":"8270_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11227-025-07153-6","volume":"81","author":"Y Liu","year":"2025","unstructured":"Liu Y, Liu B, Lin W, Yan Y, Zhang L (2025) Exploiting gaussian distribution in channel pruning for convolutional neural networks. J Supercomput 81(5):1\u201325","journal-title":"J Supercomput"},{"key":"8270_CR14","unstructured":"He Y, Han S (2018) ADC: automated deep compression and acceleration with reinforcement learning. CoRR abs\/1802.03494 arxiv:1802.03494"},{"key":"8270_CR15","doi-asserted-by":"publisher","unstructured":"Wang Z, Li C (2022) Channel pruning via lookahead search guided reinforcement learning. In: 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 3513\u20133524. https:\/\/doi.org\/10.1109\/WACV51458.2022.00357","DOI":"10.1109\/WACV51458.2022.00357"},{"key":"8270_CR16","unstructured":"Yu S, Mazaheri A, Jannesari A (2022) Topology-aware network pruning using multi-stage graph embedding and reinforcement learning. In: International Conference on Machine Learning, pp. 25656\u201325667. PMLR"},{"key":"8270_CR17","unstructured":"Liu S, Chen T, Chen X, Shen L, Mocanu DC, Wang Z, Pechenizkiy M (2022) The unreasonable effectiveness of random pruning: return of the most naive baseline for sparse training. In: International Conference on Learning Representations"},{"key":"8270_CR18","doi-asserted-by":"publisher","unstructured":"Tishby N, Zaslavsky N (2015) Deep learning and the information bottleneck principle. In: 2015 IEEE Information Theory Workshop (ITW), pp. 1\u20135. https:\/\/doi.org\/10.1109\/ITW.2015.7133169","DOI":"10.1109\/ITW.2015.7133169"},{"key":"8270_CR19","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1007\/s10489-025-06332-5","volume":"55","author":"S Lian","year":"2025","unstructured":"Lian S, Zhao Y, Pei J (2025) Daar: dual attention cooperative adaptive pruning rate by data-driven for filter pruning. Appl Intell 55:402","journal-title":"Appl Intell"},{"issue":"12","key":"8270_CR20","doi-asserted-by":"publisher","first-page":"10374","DOI":"10.1109\/TNNLS.2022.3166101","volume":"34","author":"Y Jiang","year":"2023","unstructured":"Jiang Y, Wang S, Valls V, Ko BJ, Lee W-H, Leung KK, Tassiulas L (2023) Model pruning enables efficient federated learning on edge devices. IEEE Trans Neural Netw Learn Syst 34(12):10374\u201310386. https:\/\/doi.org\/10.1109\/TNNLS.2022.3166101","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"8270_CR21","doi-asserted-by":"crossref","unstructured":"Lin M, Ji R, Wang Y, Zhang Y, Zhang B, Tian Y, Shao L (2020) Hrank: Filter pruning using high-rank feature map. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1526\u20131535","DOI":"10.1109\/CVPR42600.2020.00160"},{"issue":"23","key":"8270_CR22","doi-asserted-by":"publisher","first-page":"24506","DOI":"10.1109\/JIOT.2022.3190873","volume":"9","author":"Z Wang","year":"2022","unstructured":"Wang Z, Liu X, Huang L, Chen Y, Zhang Y, Lin Z, Wang R (2022) Qsfm: Model pruning based on quantified similarity between feature maps for ai on edge. IEEE Internet Things J 9(23):24506\u201324515","journal-title":"IEEE Internet Things J"},{"issue":"13","key":"8270_CR23","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1007\/s11227-025-07786-7","volume":"81","author":"H Liang","year":"2025","unstructured":"Liang H, Guo Q, Shao M, Zhang Q (2025) Lsa-mep: layer-wise sparsity allocation multi-metric evaluation pruning: H. liang et al. J Supercomput 81(13):1279","journal-title":"J Supercomput"},{"key":"8270_CR24","doi-asserted-by":"publisher","first-page":"131885","DOI":"10.1016\/j.neucom.2025.131885","volume":"661","author":"S Li","year":"2026","unstructured":"Li S, Chen J, Xiang J, Zhu C, Yang J, Wei X, Jiang Y, Liu Y (2026) Automatic data-free pruning via channel similarity reconstruction. Neurocomputing 661:131885. https:\/\/doi.org\/10.1016\/j.neucom.2025.131885","journal-title":"Neurocomputing"},{"issue":"1","key":"8270_CR25","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"},{"issue":"4","key":"8270_CR26","doi-asserted-by":"publisher","first-page":"6394","DOI":"10.1109\/TII.2023.3348843","volume":"20","author":"Y Xue","year":"2024","unstructured":"Xue Y, Han X, Wang Z (2024) Self-adaptive weight based on dual-attention for differentiable neural architecture search. IEEE Trans Industr Inf 20(4):6394\u20136403","journal-title":"IEEE Trans Industr Inf"},{"key":"8270_CR27","doi-asserted-by":"crossref","unstructured":"Wang Z, Li C (2022) Channel pruning via lookahead search guided reinforcement learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2029\u20132040","DOI":"10.1109\/WACV51458.2022.00357"},{"key":"8270_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.131069","volume":"306","author":"VG Gaddam","year":"2026","unstructured":"Gaddam VG, Kalaivani K, Ramakrishna KVSS, Singaraju S, Motupalli R (2026) Optimizing distributed inference in healthcare iot: reinforcement learning and explainable ai for dynamic neural network pruning. Expert Syst Appl 306:131069. https:\/\/doi.org\/10.1016\/j.eswa.2025.131069","journal-title":"Expert Syst Appl"},{"key":"8270_CR29","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. University of Toronto, Toronto, ON, Canada, pp 32\u201333"},{"issue":"3","key":"8270_CR30","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 (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115(3):211\u2013252","journal-title":"Int J Comput Vision"},{"key":"8270_CR31","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"8270_CR32","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":"8270_CR33","unstructured":"Ye J, Lu X, Lin Z, Wang JZ (2018) Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers. CoRR abs\/1802.00124 arxiv:1802.00124"},{"key":"8270_CR34","doi-asserted-by":"crossref","unstructured":"He Y, Zhang X, Sun J (2017) Channel pruning for accelerating very deep neural networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 1398\u20131406","DOI":"10.1109\/ICCV.2017.155"},{"key":"8270_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107461","volume":"107","author":"J-H Luo","year":"2020","unstructured":"Luo J-H, Wu J (2020) Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference. Pattern Recogn 107:107461","journal-title":"Pattern Recogn"},{"key":"8270_CR36","doi-asserted-by":"crossref","unstructured":"Lin M, Ji R, Zhang Y, Zhang B, Wu Y, Tian Y (2021) Channel pruning via automatic structure search. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. IJCAI\u201920","DOI":"10.24963\/ijcai.2020\/94"},{"key":"8270_CR37","unstructured":"Zhuang Z, Tan M, Zhuang B, Liu J, Guo Y, Wu Q, Huang J, Zhu J (2018) Discrimination-aware channel pruning for deep neural networks. In: Advances in neural information processing systems, vol 31. Curran Associates, Inc."},{"key":"8270_CR38","first-page":"10936","volume":"33","author":"Y Tang","year":"2020","unstructured":"Tang Y, Wang Y, Xu Y, Tao D, Xu C, Xu C, Xu C (2020) Scop: Scientific control for reliable neural network pruning. Adv Neural Inf Process Syst 33:10936\u201310947","journal-title":"Adv Neural Inf Process Syst"},{"issue":"8","key":"8270_CR39","doi-asserted-by":"publisher","first-page":"4035","DOI":"10.1109\/TPAMI.2021.3066410","volume":"44","author":"J Liu","year":"2022","unstructured":"Liu J, Zhuang B, Zhuang Z, Guo Y, Huang J, Zhu J, Tan M (2022) Discrimination-aware network pruning for deep model compression. IEEE Trans Pattern Anal Mach Intell 44(8):4035\u20134051. https:\/\/doi.org\/10.1109\/TPAMI.2021.3066410","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8270_CR40","doi-asserted-by":"publisher","unstructured":"Gao S, Huang F, Pei J, Huang H (2020) Discrete model compression with resource constraint for deep neural networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1896\u20131905. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00197","DOI":"10.1109\/CVPR42600.2020.00197"},{"key":"8270_CR41","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1007\/978-3-031-20083-0_20","volume-title":"Computer vision\u2014ECCV 2022","author":"S Gao","year":"2022","unstructured":"Gao S, Huang F, Zhang Y, Huang H (2022) Disentangled differentiable network pruning. In: Avidan S, Brostow G, Ciss\u00e9 M, Farinella GM, Hassner T (eds) Computer vision\u2014ECCV 2022. Springer, Cham, pp 328\u2013345"},{"key":"8270_CR42","doi-asserted-by":"crossref","unstructured":"Liu Z, Li J, Shen Z, Huang G, Yan S, Zhang C (2017) Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV.2017.298"},{"key":"8270_CR43","doi-asserted-by":"crossref","unstructured":"Wang W, Fu C, Guo J, Cai D, He X (2019) Cop: Customized deep model compression via regularized correlation-based filter-level pruning. arXiv preprint arXiv:1906.10337","DOI":"10.24963\/ijcai.2019\/525"},{"key":"8270_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109508","volume":"140","author":"S Guo","year":"2023","unstructured":"Guo S, Lai B, Yang S, Zhao J, Shen F (2023) Sensitivity pruner: Filter-level compression algorithm for deep neural networks. Pattern Recogn 140:109508","journal-title":"Pattern Recogn"},{"key":"8270_CR45","doi-asserted-by":"crossref","unstructured":"Dong X, Huang J, Yang Y, Yan S (2017) More is less: a more complicated network with less inference complexity. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1895\u20131903","DOI":"10.1109\/CVPR.2017.205"},{"key":"8270_CR46","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/978-3-030-58583-9_15","volume-title":"Computer vision\u2014ECCV 2020","author":"C Herrmann","year":"2020","unstructured":"Herrmann C, Bowen RS, Zabih R (2020) Channel selection using gumbel softmax. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) Computer vision\u2014ECCV 2020. Springer, Cham, pp 241\u2013257"},{"key":"8270_CR47","doi-asserted-by":"publisher","unstructured":"Zhang Y, Gao S, Huang H (2021) Exploration and estimation for model compression. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 477\u2013486 . https:\/\/doi.org\/10.1109\/ICCV48922.2021.00054","DOI":"10.1109\/ICCV48922.2021.00054"},{"key":"8270_CR48","doi-asserted-by":"crossref","unstructured":"Lin S, Ji R, Li Y, Wu Y, Huang F, Zhang B (2018) Accelerating convolutional networks via global & dynamic filter pruning. In: IJCAI, vol. 2, p. 8 Stockholm","DOI":"10.24963\/ijcai.2018\/336"},{"key":"8270_CR49","unstructured":"Lee J, Park S, Mo S, Ahn S, Shin J (2020) A deeper look at the layerwise sparsity of magnitude-based pruning. CoRR abs\/2010.07611 arxiv:2010.07611"},{"key":"8270_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2025.122845","volume":"729","author":"AM Shaikh","year":"2026","unstructured":"Shaikh AM, Kang Y, Kumar A, Zhao Y-B (2026) Dynamic filter pruning via unified importance and redundancy. Inf Sci 729:122845. https:\/\/doi.org\/10.1016\/j.ins.2025.122845","journal-title":"Inf Sci"},{"key":"8270_CR51","doi-asserted-by":"publisher","unstructured":"Yin H, Molchanov P, Alvarez JM, Li Z, Mallya A, Hoiem D, Jha NK, Kautz J (2020) Dreaming to distill: Data-free knowledge transfer via deepinversion. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8712\u20138721 . https:\/\/doi.org\/10.1109\/CVPR42600.2020.00874","DOI":"10.1109\/CVPR42600.2020.00874"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08270-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08270-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08270-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T13:57:02Z","timestamp":1770645422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08270-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":51,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["8270"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08270-6","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]},"assertion":[{"value":"15 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2026","order":3,"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"}}],"article-number":"140"}}