{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T13:59:27Z","timestamp":1726408767339},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030805678"},{"type":"electronic","value":"9783030805685"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-80568-5_20","type":"book-chapter","created":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T13:04:53Z","timestamp":1624453493000},"page":"232-243","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Squeeze-and-Threshold Based Quantization for Low-Precision Neural Networks"],"prefix":"10.1007","author":[{"given":"Binyi","family":"Wu","sequence":"first","affiliation":[]},{"given":"Bernd","family":"Waschneck","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Mayr","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,1]]},"reference":[{"unstructured":"Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 265\u2013283 (2016)","key":"20_CR1"},{"doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., AlZawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1\u20136 (2017)","key":"20_CR2","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"unstructured":"Bethge, J., Bartz, C., Yang, H., Chen, Y., Meinel, C.: MeliusNet: can binary neural networks achieve MobileNet-level accuracy? CoRR, abs\/2001.05936 (2020)","key":"20_CR3"},{"doi-asserted-by":"crossref","unstructured":"Bethge, J., Yang, H., Bornstein, M., Meinel, C.: BinaryDenseNet: developing an archi-tecture for binary neural networks. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshops, ICCV Workshops 2019, Seoul, Korea (South), 27-28 October 2019, pp. 1951\u20131960. IEEE (2019)","key":"20_CR4","DOI":"10.1109\/ICCVW.2019.00244"},{"unstructured":"Bulat, A., Tzimiropoulos, G.: XNOR-Net++: improved binary neural networks. In: 30th British Machine Vision Conference 2019, BMVC 2019, Cardiff, UK, 9-12 September2019, p. 62. BMVA Press (2019)","key":"20_CR5"},{"doi-asserted-by":"crossref","unstructured":"Cai, Z., He, X., Sun, J., Vasconce-los, N.: Deep learning with low precision by half-wave Gaussian quantization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","key":"20_CR6","DOI":"10.1109\/CVPR.2017.574"},{"unstructured":"Choi, J., Wang, Z., Venkataramani, S., Chuang, P.I., Srinivasan, V., Gopalakrishnan, K.: PACT: parameterized clipping activation for quantized neural networks. CoRR, abs\/1805.06085 (2018)","key":"20_CR7"},{"unstructured":"Le Cun, Y., Denker, J.S., Solla, S.A.: Optimal Brain Damage, pp. 598\u2013605. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1990)","key":"20_CR8"},{"unstructured":"Darabi, S., Belbahri, M., Courbariaux, M., Nia, V.P.: BNN+: improved binary network training. CoRR, abs\/1812.11800 (2018)","key":"20_CR9"},{"doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR09 (2009)","key":"20_CR10","DOI":"10.1109\/CVPR.2009.5206848"},{"unstructured":"Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural net-works. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, NIPS 2015, vol. 1, pp. 1135\u20131143. MIT Press, Cambridge, MA, USA (2015)","key":"20_CR11"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27-30 June 2016, pp. 770\u2013778. IEEE Computer Society (2016)","key":"20_CR12","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"crossref","unstructured":"Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sign. Process. Mag. 29(6), 82\u201397 (2012)","key":"20_CR13","DOI":"10.1109\/MSP.2012.2205597"},{"unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. CoRR,abs\/1704.04861 (2017)","key":"20_CR14"},{"doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","key":"20_CR15","DOI":"10.1109\/CVPR.2018.00745"},{"unstructured":"Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks. In: Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 5\u201310 December 2016, Barcelona, Spain, pp. 4107\u20134115 (2016)","key":"20_CR16"},{"unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal co-variate shift. In: Bach, F.R., Blei, D.M. (eds.) Proceedings of the 32nd International Conference on Ma-chine Learning, ICML 2015, Lille, France, 6-11 July 2015, volume 37 of JMLR Workshop and Conference Proceedings, vol. 37, pp. 448\u2013456. JMLR.org (2015)","key":"20_CR17"},{"doi-asserted-by":"crossref","unstructured":"Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18-22 June 2018, pp. 2704\u20132713. IEEE Computer Society (2018)","key":"20_CR18","DOI":"10.1109\/CVPR.2018.00286"},{"unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. Technical report (2009)","key":"20_CR19"},{"doi-asserted-by":"crossref","unstructured":"Liu, Z., Shen, Z., Savvides, M., Cheng, K.-T.: ReActNet: towards precise binary neural network with generalized activation functions. CoRR,abs\/2003.03488 (2020)","key":"20_CR20","DOI":"10.1007\/978-3-030-58568-6_9"},{"doi-asserted-by":"crossref","unstructured":"Liu, Z., Wu, B., Luo, W., Yand, X., Liu, W., Cheng, K.-T.: Bi-Real Net: enhancing the performance of 1-bit CNNs with improved representational capability and advanced training algorithm. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 722\u2013737 (2018)","key":"20_CR21","DOI":"10.1007\/978-3-030-01267-0_44"},{"issue":"1","key":"20_CR22","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1007\/s11263-019-01227-8","volume":"128","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Baoyuan, W., Luo, W., Yand, X., Liu, W., Cheng, K.-T.: Bi-Real Net: binarizing deep network towards real-network performance. Int. J. Comput. Vis. (IJCV) 128(1), 202\u2013219 (2020)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. OpenReview.net (2017)","key":"20_CR23"},{"unstructured":"Louizos, C., Reisser, M., Blankevoort, T., Gavves, E.-S., Welling, M.: Relaxed quantization for discretized neural networks. In: 7th International Conference on Learning Representations, ICLR 2019, 6\u20139 May 2019, New Orleans, LA, USA. OpenReview.net (2019)","key":"20_CR24"},{"unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8\u201314 December 2019, Vancouver, BC, Canada, pp. 8024\u20138035 (2019)","key":"20_CR25"},{"doi-asserted-by":"publisher","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: ImageNet classification using bi-nary convolutional neural networks. In: ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016, Proceedings, Part IV, vol. 9908. Lecture Notes in Computer Science, pp. 525\u2013542. Springer (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_32","key":"20_CR26","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"20_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"unstructured":"Zhou, S., Ni, Z., Zhou, X., Wen, H., Wu, Y., Zou, Y.: DoReFa-Net: training low bitwidth convolutional neural networks with low bitwidth gradients. CoRR, abs\/1606.06160 (2016)","key":"20_CR28"},{"unstructured":"Esser, S.K., McKinstry, J.L., Bablani, D., Appuswamy, R., Modha, D.S.: Learned step size quantization. In: 8th International Conference on Learning Representations (ICLR), 2020, Addis Ababa, Ethiopia, 26\u201330 April 2020","key":"20_CR29"},{"doi-asserted-by":"crossref","unstructured":"Zhuang, B., Liu, L., Tan, M., Shen, C., Reid, I.D.: Training quantized neural networks with a full-precision auxiliary module. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 13\u201319 June 2020, Seattle, WA, USA (2020)","key":"20_CR30","DOI":"10.1109\/CVPR42600.2020.00156"},{"doi-asserted-by":"crossref","unstructured":"Qin, H., et al.: Forward and backward information retention for accurate binary neural networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 13\u201319 June 2020, Seattle, WA, USA (2020)","key":"20_CR31","DOI":"10.1109\/CVPR42600.2020.00232"}],"container-title":["Proceedings of the International Neural Networks Society","Proceedings of the 22nd Engineering Applications of Neural Networks Conference"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-80568-5_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T03:05:30Z","timestamp":1656385530000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-80568-5_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030805678","9783030805685"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-80568-5_20","relation":{},"ISSN":["2661-8141","2661-815X"],"issn-type":[{"type":"print","value":"2661-8141"},{"type":"electronic","value":"2661-815X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering Applications of Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Crete","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.eann2021.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}