{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:37:44Z","timestamp":1787319464776,"version":"build-2736575974"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The National Key Research and Development Program of China under Grant","award":["No. 2017YFB1401000"],"award-info":[{"award-number":["No. 2017YFB1401000"]}]},{"name":"The Key Research and Development Program of Shanxi Province under Grant","award":["No. 201903D421007"],"award-info":[{"award-number":["No. 201903D421007"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s10994-022-06193-w","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T17:03:08Z","timestamp":1656003788000},"page":"3161-3180","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Pruning convolutional neural networks via filter similarity analysis"],"prefix":"10.1007","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4324-9047","authenticated-orcid":false,"given":"Lili","family":"Geng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoning","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"6193_CR1","unstructured":"Anwar, S., & Sung, W. (2016). Retrieved August 20, 2020 from Coarse pruning of convolutional neural networks with random masks. https:\/\/openreview.net\/forum?id=HkvS3Mqxe"},{"key":"6193_CR2","unstructured":"Han, S., Mao, H., & Dally, W. J. (2015). Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. arXiv Preprint arXiv:1510.00149"},{"key":"6193_CR3","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 (CVPR) (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"key":"6193_CR4","unstructured":"Howard, A. G., 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":"6193_CR5","unstructured":"Hu, H., Peng, R., Tai, Y. W., & Tang, C. K. (2016). Network trimming: A data-driven neuron pruning approach towards efficient deep architectures. arXiv preprint arXiv:1607.03250"},{"key":"6193_CR6","unstructured":"Hu, Y., Sun, S., Li, J., Wang, X., & Gu, Q. (2018). A novel channel pruning method for deep neural network compression. arXiv preprint arXiv:1805.11394"},{"key":"6193_CR7","doi-asserted-by":"crossref","unstructured":"Huang, Z., & Wang, N. (2018). Data-driven sparse structure selection for deep neural networks. In Proceedings of the European conference on computer vision (ECCV) (pp. 304\u2013320).","DOI":"10.1007\/978-3-030-01270-0_19"},{"key":"6193_CR8","unstructured":"Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360"},{"key":"6193_CR9","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097\u20131105). Curran Associates, Inc."},{"issue":"11","key":"6193_CR10","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., & Bottou, L. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proceedings of the IEEE"},{"key":"6193_CR11","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., & Graf, H. P. (2017). Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710"},{"key":"6193_CR12","doi-asserted-by":"crossref","unstructured":"Lin, S., Ji, R., Yan, C., Zhang, B., Cao, L., Ye, Q., Huang, F., & Doermann, D. (2019). Towards optimal structured CNN pruning via generative adversarial learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2785\u20132794).","DOI":"10.1109\/CVPR.2019.00290"},{"key":"6193_CR13","unstructured":"Luo, J., & Wu, J. (2017). An entropy-based pruning method for CNN compression. arXiv preprint arXiv:1706.05791"},{"key":"6193_CR14","doi-asserted-by":"crossref","unstructured":"Luo, J. H., Wu, J., & Lin, W. (2017). ThiNet: A filter level pruning method for deep neural network compression. arXiv preprint arXiv:1707.06342","DOI":"10.1109\/ICCV.2017.541"},{"key":"6193_CR15","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H. T., & Sun, J. (2018). ShuffleNet V2: practical guidelines for efficient CNN architecture design. In Proceedings of the European conference on computer vision (ECCV), Munich, Germany (pp. 116\u2013131).","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"6193_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-018-01001-9","author":"D Mittal","year":"2019","unstructured":"Mittal, D., Bhardwaj, S., Khapra, M. M., & Ravindran, B. (2019). Studying the plasticity in deep convolutional neural networks using random pruning. Machine Vision and Applications. https:\/\/doi.org\/10.1007\/s00138-018-01001-9","journal-title":"Machine Vision and Applications"},{"key":"6193_CR17","unstructured":"Molchanov, P., Tyree, S., Karras, T., Aila, T., & Kautz, J. (2016). Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440"},{"key":"6193_CR18","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 (CVPR) (pp. 4510\u20134520).","DOI":"10.1109\/CVPR.2018.00474"},{"key":"6193_CR19","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"6193_CR20","unstructured":"Singh, P., Verma, V. K., Rai, P., & Namboodiri, V. P. (2018). Leveraging filter correlations for deep model compression. arXiv preprint arXiv:1811.10559"},{"key":"6193_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1\u20139).","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"6193_CR22","doi-asserted-by":"crossref","unstructured":"Yang, T. J., Chen, Y. H., & Sze, V. (2017). Designing energy-efficient convolutional neural networks using energy-aware pruning. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 6071\u20136079).","DOI":"10.1109\/CVPR.2017.643"},{"key":"6193_CR23","doi-asserted-by":"crossref","unstructured":"Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In Proceedings of the European conference on computer vision (ECCV) (pp. 818\u2013833). Springer.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"6193_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 6848\u20136856).","DOI":"10.1109\/CVPR.2018.00716"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06193-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-022-06193-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06193-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T13:34:34Z","timestamp":1687527274000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-022-06193-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,23]]},"references-count":24,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["6193"],"URL":"https:\/\/doi.org\/10.1007\/s10994-022-06193-w","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,23]]},"assertion":[{"value":"29 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}