{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:10:41Z","timestamp":1781763041945,"version":"3.54.5"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s11760-021-02108-9","type":"journal-article","created":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T07:02:42Z","timestamp":1643439762000},"page":"1541-1548","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Knowledge distillation-based performance transferring for LSTM-RNN model acceleration"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1931-7203","authenticated-orcid":false,"given":"Hongbin","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruowu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimin","family":"Long","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangjun","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,29]]},"reference":[{"issue":"8","key":"2108_CR1","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"1","key":"2108_CR2","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1109\/TASLP.2017.2764271","volume":"26","author":"Z Tang","year":"2017","unstructured":"Tang, Z., Wang, D., Chen, Y., Li, L., Abel, A.: Phonetic temporal neural model for language identification. IEEE\/ACM Trans. Audio Speech Lang. Process. 26(1), 134\u2013144 (2017)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"2108_CR3","doi-asserted-by":"crossref","unstructured":"Graves, A., Jaitly, N., Mohamed, A.r.: Hybrid speech recognition with deep bidirectional lstm. In: 2013 IEEE workshop on automatic speech recognition and understanding, pp. 273\u2013278. IEEE (2013)","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"2108_CR4","doi-asserted-by":"crossref","unstructured":"Jedrzejewska, M.K., Zjawinski, A., Stasiak, B.: Generating musical expression of midi music with lstm neural network. In: 2018 11th International Conference on Human System Interaction (HSI), pp. 132\u2013138. IEEE (2018)","DOI":"10.1109\/HSI.2018.8431033"},{"key":"2108_CR5","doi-asserted-by":"crossref","unstructured":"Kumar, S.D., Subha, D.: Prediction of depression from eeg signal using long short term memory (lstm). In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1248\u20131253. IEEE (2019)","DOI":"10.1109\/ICOEI.2019.8862560"},{"key":"2108_CR6","doi-asserted-by":"crossref","unstructured":"Chen, X., Du, J., Zhang, H.: Lipreading with densenet and resbi-lstm, pp. 1\u20139. Signal, Image and Video Processing pp (2020)","DOI":"10.1007\/s11760-019-01630-1"},{"issue":"10","key":"2108_CR7","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.1140\/epjst\/e2019-900046-x","volume":"228","author":"K Smagulova","year":"2019","unstructured":"Smagulova, K., James, A.P.: A survey on LSTM memristive neural network architectures and applications. Eur. Phys. J. Spec. Top. 228(10), 2313\u20132324 (2019)","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"2108_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, C., Gong, L., Lu, Y., Sun, F., Xu, C., Li, X., Zhou, X.: A power-efficient accelerator based on fpgas for lstm network. In: 2017 IEEE International Conference on Cluster Computing (CLUSTER), pp. 629\u2013630. IEEE (2017)","DOI":"10.1109\/CLUSTER.2017.45"},{"key":"2108_CR9","doi-asserted-by":"publisher","first-page":"78923","DOI":"10.1109\/ACCESS.2020.2988727","volume":"8","author":"H Ma","year":"2020","unstructured":"Ma, H., Xu, G., Meng, H., Wang, M., Yang, S., Wu, R., Wang, W.: Cross model deep learning scheme for automatic modulation classification. IEEE Access 8, 78923\u201378931 (2020)","journal-title":"IEEE Access"},{"key":"2108_CR10","doi-asserted-by":"crossref","unstructured":"Kayode, O., Tosun, A.S.: Lirul: A lightweight lstm based model for remaining useful life estimation at the edge. In: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), vol.\u00a02, pp. 177\u2013182. IEEE (2019)","DOI":"10.1109\/COMPSAC.2019.10203"},{"key":"2108_CR11","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008 (2008)","DOI":"10.1145\/1390156.1390294"},{"key":"2108_CR12","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"2108_CR13","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)"},{"key":"2108_CR14","unstructured":"Zagoruyko, S., Komodakis, N.: Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928 (2016)"},{"key":"2108_CR15","doi-asserted-by":"crossref","unstructured":"Zhou, G., Fan, Y., Cui, R., Bian, W., Zhu, X., Gai, K.: Rocket launching: A universal and efficient framework for training well-performing light net. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11601"},{"key":"2108_CR16","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.neucom.2021.04.086","volume":"456","author":"H Ma","year":"2021","unstructured":"Ma, H., Yang, S., Feng, D., Jiao, L., Zhang, L.: Progressive mimic learning: A new perspective to train lightweight CNN models. Neurocomput. 456, 220\u2013231 (2021)","journal-title":"Neurocomput."},{"key":"2108_CR17","unstructured":"Xu, Z., Hsu, Y.C., Huang, J.: Training shallow and thin networks for acceleration via knowledge distillation with conditional adversarial networks. arXiv preprint arXiv:1709.00513 (2017)"},{"issue":"6","key":"2108_CR18","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TMI.2018.2820120","volume":"37","author":"TM Quan","year":"2018","unstructured":"Quan, T.M., Nguyen-Duc, T., Jeong, W.K.: Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans. Med. Imag. 37(6), 1488\u20131497 (2018)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"3","key":"2108_CR19","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1109\/TCCN.2018.2835460","volume":"4","author":"S Rajendran","year":"2018","unstructured":"Rajendran, S., Meert, W., Giustiniano, D., Lenders, V., Pollin, S.: Deep learning models for wireless signal classification with distributed low-cost spectrum sensors. IEEE Trans. Cogn. Commun. Netw. 4(3), 433\u2013445 (2018)","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"2108_CR20","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"2108_CR21","unstructured":"Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: Advances in neural information processing systems, pp. 1019\u20131027 (2016)"},{"key":"2108_CR22","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 315\u2013323 (2011)"},{"key":"2108_CR23","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et\u00a0al.: Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-021-02108-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-021-02108-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-021-02108-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T11:14:21Z","timestamp":1659093261000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-021-02108-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,29]]},"references-count":23,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["2108"],"URL":"https:\/\/doi.org\/10.1007\/s11760-021-02108-9","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,29]]},"assertion":[{"value":"12 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}