{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T01:30:03Z","timestamp":1768872603912,"version":"3.49.0"},"reference-count":42,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2025,7,1]]},"DOI":"10.1587\/transinf.2023edp7165","type":"journal-article","created":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T22:15:31Z","timestamp":1735251331000},"page":"734-743","source":"Crossref","is-referenced-by-count":1,"title":["POEM: Pruning with Output Error Minimization for Compressing Deep Neural Networks"],"prefix":"10.1587","volume":"E108.D","author":[{"given":"Koji","family":"KAMMA","sequence":"first","affiliation":[{"name":"Wakayama University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toshikazu","family":"WADA","sequence":"additional","affiliation":[{"name":"Wakayama University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] K. Kamma and T. Wada, \u201cPruning based on activation function outputs for efficient neural networks,\u201d IEEE Conference on Systems, Man, and Cybernetics (SMC), 2023. 10.1109\/smc53992.2023.10393934","DOI":"10.1109\/SMC53992.2023.10393934"},{"key":"2","unstructured":"[2] A. Krizhevsky, I. Sutskever, and G.E. Hinton, \u201cImagenet classification with deep convolutional neural networks,\u201d Advances in Neural Information Processing Systems, ed. F. Pereira, C. Burges, L. Bottou, and K. Weinberger, Curran Associates, Inc., 2012."},{"key":"3","unstructured":"[3] A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, \u201cMobilenets: Efficient convolutional neural networks for mobile vision applications,\u201d 2017."},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] S. Zagoruyko and N. Komodakis, \u201cWide residual networks,\u201d BMVC, 2016. 10.5244\/c.30.87","DOI":"10.5244\/C.30.87"},{"key":"6","unstructured":"[6] Y. LeCun, J.S. Denker, and S.A. Solla, \u201cOptimal brain damage,\u201d Proceedings of Conference on Neural Information Processing Systems (NIPS), pp.598-605, 1990."},{"key":"7","unstructured":"[7] P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz, \u201cPruning convolutional neural networks for resource efficient inference,\u201d International Conference on Learning Representations, 2017."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] J.-H. Luo, J. Wu, and W. Lin, \u201cThinet: A filter level pruning method for deep neural network compression,\u201d The IEEE International Conference on Computer Vision (ICCV), pp.5068-5076, 2017. 10.1109\/iccv.2017.541","DOI":"10.1109\/ICCV.2017.541"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] Y. He, X. Zhang, and J. Sun, \u201cChannel pruning for accelerating very deep neural networks,\u201d The IEEE International Conference on Computer Vision (ICCV), Oct. 2017. 10.1109\/iccv.2017.155","DOI":"10.1109\/ICCV.2017.155"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] K. Kamma and T. Wada, \u201cReap: A method for pruning convolutional neural networks with performance preservation,\u201d IEICE Transactions on Information and Systems, vol.E104-D, no.1, pp.194-202, 2021. 10.1587\/transinf.2020edp7049","DOI":"10.1587\/transinf.2020EDP7049"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han, \u201cAmc: Automl for model compression and acceleration on mobile devices,\u201d Proceedings of the European Conference on Computer Vision (ECCV), Sept. 2018. 10.1007\/978-3-030-01234-2_48","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] K. Kamma, S. Inoue, and T. Wada, \u201cPruning ratio optimization with layer-wise pruning method for accelerating convolutional neural networks,\u201d IEICE Transactions on Information and Systems, vol.E105-D, no.1, pp.161-169, 2022. 10.1587\/transinf.2021edp7096","DOI":"10.1587\/transinf.2021EDP7096"},{"key":"13","unstructured":"[13] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition.,\u201d ICLR, ed. Y. Bengio and Y. LeCun, 2015."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, \u201cImagenet: A large-scale hierarchical image database,\u201d 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.248-255, 2009. 10.1109\/cvpr.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"15","unstructured":"[15] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, \u201cThe caltech-ucsd birds-200-2011 dataset,\u201d Tech. Rep. CNS-TR-2011-001, California Institute of Technology, 2011."},{"key":"16","unstructured":"[16] A. Krizhevsky, V. Nair, and G. Hinton, \u201cCifar-10 (canadian institute for advanced research),\u201d"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] C. Jiang, G. Li, C. Qian, and K. Tang, \u201cEfficient dnn neuron pruning by minimizing layer-wise nonlinear reconstruction error,\u201d Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp.2298-2304, International Joint Conferences on Artificial Intelligence Organization, 2018. 10.24963\/ijcai.2018\/318","DOI":"10.24963\/ijcai.2018\/318"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] Y. Li, K. Adamczewski, W. Li, S. Gu, R. Timofte, and L. Van Gool, \u201cRevisiting random channel pruning for neural network compression,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.191-201, June 2022. 10.1109\/cvpr52688.2022.00029","DOI":"10.1109\/CVPR52688.2022.00029"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] Y. He, G. Kang, X. Dong, Y. Fu, and Y. Yang, \u201cSoft filter pruning for accelerating deep convolutional neural networks,\u201d Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI\u201918, pp.2234-2240, AAAI Press, 2018. 10.24963\/ijcai.2018\/309","DOI":"10.24963\/ijcai.2018\/309"},{"key":"20","unstructured":"[20] B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Pensky, \u201cSparse convolutional neural networks,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015. 10.1109\/cvpr.2015.7298681"},{"key":"21","unstructured":"[21] S. Han, H. Mao, and W.J. Dally, \u201cDeep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,\u201d International Conference on Learning Representations (ICLR), 2016."},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] J. Xue, J. Li, and Y. Gong, \u201cRestructuring of deep neural network acoustic models with singular value decomposition,\u201d Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, pp.2365-2369, 2013. 10.21437\/interspeech.2013-552","DOI":"10.21437\/Interspeech.2013-552"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] H. Kim, M.U.K. Khan, and C.-M. Kyung, \u201cEfficient neural network compression,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. 10.1109\/cvpr.2019.01285","DOI":"10.1109\/CVPR.2019.01285"},{"key":"24","unstructured":"[24] E.L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus, \u201cExploiting linear structure within convolutional networks for efficient evaluation,\u201d Advances in Neural Information Processing Systems, ed. Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, Curran Associates, Inc., 2014."},{"key":"25","unstructured":"[25] M. Courbariaux, Y. Bengio, and J. David, \u201cBinaryconnect: Training deep neural networks with binary weights during propagations,\u201d Proceedings of Conference on Neural Information Processing Systems (NIPS), pp.3123-3131, 2015."},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] Z. Liu, K.-T. Cheng, D. Huang, E. Xing, and Z. Shen, \u201cNonuniform-to-uniform quantization: Towards accurate quantization via generalized straight-through estimation,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.4942-4952, June 2022. 10.1109\/cvpr52688.2022.00489","DOI":"10.1109\/CVPR52688.2022.00489"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] T. He, Y. Fan, Y. Qian, T. Tan, and K. Yu, \u201cReshaping deep neural network for fast decoding by node-pruning,\u201d Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.245-249, 2014. 10.1109\/icassp.2014.6853595","DOI":"10.1109\/ICASSP.2014.6853595"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang, \u201cLearning efficient convolutional networks through network slimming,\u201d ICCV, 2017. 10.1109\/iccv.2017.298","DOI":"10.1109\/ICCV.2017.298"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] X. Ding, G. Ding, Y. Guo, and J. Han, \u201cCentripetal sgd for pruning very deep convolutional networks with complicated structure,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. 10.1109\/cvpr.2019.00508","DOI":"10.1109\/CVPR.2019.00508"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] Y. Guo, H. Yuan, J. Tan, Z. Wang, S. Yang, and J. Liu, \u201cGdp: Stabilized neural network pruning via gates with differentiable polarization,\u201d Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp.5239-5250, Oct. 2021. 10.1109\/iccv48922.2021.00519","DOI":"10.1109\/ICCV48922.2021.00519"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] Y. He, Y. Ding, P. Liu, L. Zhu, H. Zhang, and Y. Yang, \u201cLearning filter pruning criteria for deep convolutional neural networks acceleration,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 10.1109\/cvpr42600.2020.00208","DOI":"10.1109\/CVPR42600.2020.00208"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] S. Gao, F. Huang, Y. Zhang, and H. Huang, \u201cDisentangled differentiable network pruning,\u201d Computer Vision - ECCV 2022, ed. S. Avidan, G. Brostow, M. Ciss\u00e9, G.M. Farinella, and T. Hassner, Cham, pp.328-345, Springer Nature Switzerland, 2022. 10.1007\/978-3-031-20083-0_20","DOI":"10.1007\/978-3-031-20083-0_20"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] A. Ganjdanesh, S. Gao, and H. Huang, \u201cInterpretations steered network pruning via amortized inferred saliency maps,\u201d Computer Vision - ECCV 2022, ed. S. Avidan, G. Brostow, M. Ciss\u00e9, G.M. Farinella, and T. Hassner, Cham, pp.278-296, Springer Nature Switzerland, 2022. 10.1007\/978-3-031-19803-8_17","DOI":"10.1007\/978-3-031-19803-8_17"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] S. Gao, F. Huang, J. Pei, and H. Huang, \u201cDiscrete model compression with resource constraint for deep neural networks,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 10.1109\/cvpr42600.2020.00197","DOI":"10.1109\/CVPR42600.2020.00197"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] S. Gao, Z. Zhang, Y. Zhang, F. Huang, and H. Huang, \u201cStructural alignment for network pruning through partial regularization,\u201d Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp.17356-17366, Oct. 2023. 10.1109\/iccv51070.2023.01596","DOI":"10.1109\/ICCV51070.2023.01596"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] Y. Li, S. Gu, C. Mayer, L.V. Gool, and R. Timofte, \u201cGroup sparsity: The hinge between filter pruning and decomposition for network compression,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 10.1109\/cvpr42600.2020.00804","DOI":"10.1109\/CVPR42600.2020.00804"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] R. Yu, A. Li, C.-F. Chen, J.-H. Lai, V.I. Morariu, X. Han, M. Gao, C.-Y. Lin, and L.S. Davis, \u201cNisp: Pruning networks using neuron importance score propagation,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018. 10.1109\/cvpr.2018.00958","DOI":"10.1109\/CVPR.2018.00958"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] J.-H. Luo and J. Wu, \u201cNeural network pruning with residual-connections and limited-data,\u201d The IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.1455-1464, June 2020. 10.1109\/cvpr42600.2020.00153","DOI":"10.1109\/CVPR42600.2020.00153"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[39] Y. He, P. Liu, Z. Wang, Z. Hu, and Y. Yang, \u201cFilter pruning via geometric median for deep convolutional neural networks acceleration,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. 10.1109\/cvpr.2019.00447","DOI":"10.1109\/CVPR.2019.00447"},{"key":"40","doi-asserted-by":"crossref","unstructured":"[40] Z. Liu, H. Mu, X. Zhang, Z. Guo, X. Yang, K.T. Cheng, and J. Sun, \u201cMetapruning: Meta learning for automatic neural network channel pruning,\u201d Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Oct. 2019. 10.1109\/iccv.2019.00339","DOI":"10.1109\/ICCV.2019.00339"},{"key":"41","doi-asserted-by":"crossref","unstructured":"[41] Y. Li, S. Gu, K. Zhang, L. Van Gool, and R. Timofte, \u201cDhp: Differentiable meta pruning via hypernetworks,\u201d Computer Vision - ECCV 2020, ed. A. Vedaldi, H. Bischof, T. Brox, and J.M. Frahm, Cham, pp.608-624, Springer International Publishing, 2020. 10.1007\/978-3-030-58598-3_36","DOI":"10.1007\/978-3-030-58598-3_36"},{"key":"42","unstructured":"[42] A. Mishra, J.A. Latorre, J. Pool, D. Stosic, D. Stosic, G. Venkatesh, C. Yu, and P. Micikevicius, \u201cAccelerating sparse deep neural networks,\u201d 2021."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/7\/E108.D_2023EDP7165\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T03:35:39Z","timestamp":1751686539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/7\/E108.D_2023EDP7165\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":42,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2023edp7165","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"article-number":"2023EDP7165"}}