{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:27:51Z","timestamp":1750220871467,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T00:00:00Z","timestamp":1576800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,12,20]]},"DOI":"10.1145\/3377713.3377721","type":"proceedings-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T10:07:26Z","timestamp":1581070046000},"page":"43-48","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Binary Convolutional Neural Network with High Accuracy and Compression Rate"],"prefix":"10.1145","author":[{"given":"Songwei","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of VLSI design, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of VLSI design, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,2,7]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Advances in neural information processing systems, 1097--1105","author":"Krizhevsky A.","year":"2012","unstructured":"A. Krizhevsky , I. Sutskever , and G.E. Hinton ( 2012 ). Imagenet classification with deep convolutional neural networks . In Advances in neural information processing systems, 1097--1105 A. Krizhevsky, I. Sutskever, and G.E.Hinton (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, 1097--1105"},{"key":"e_1_3_2_1_2_1","volume-title":"Very deep convolutional networks for large-scale image recognition. https:\/\/arxiv.org\/abs\/1409.1556v6","author":"Simonyan K.","year":"2012","unstructured":"K. Simonyan , A Zisserman ( 2012 ). Very deep convolutional networks for large-scale image recognition. https:\/\/arxiv.org\/abs\/1409.1556v6 K. Simonyan, A Zisserman (2012). Very deep convolutional networks for large-scale image recognition. https:\/\/arxiv.org\/abs\/1409.1556v6"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.446"},{"key":"e_1_3_2_1_7_1","volume-title":"Simultaneous Clustering and Tracklet Linking for Multi-face Tracking in Videos. In IEEE International Conference on Computer Vision, 2856--2863","author":"Wu B.","year":"2013","unstructured":"B. Wu , S. Lyu , B. Hu and Q. Ji ( 2013 ). Simultaneous Clustering and Tracklet Linking for Multi-face Tracking in Videos. In IEEE International Conference on Computer Vision, 2856--2863 . B. Wu, S. Lyu, B. Hu and Q. Ji (2013). Simultaneous Clustering and Tracklet Linking for Multi-face Tracking in Videos. In IEEE International Conference on Computer Vision, 2856--2863."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.10.022"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.23"},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning, 3286--3295","author":"Luo W.","year":"2018","unstructured":"W. Luo , P. Sun , W. Liu , T. Zhang and Y. Wang ( 2018 ). End-to-end Active Object Tracking via Reinforcement Learning . In Proceedings of the 35th International Conference on Machine Learning, 3286--3295 . W. Luo, P. Sun, W. Liu, T. Zhang and Y. Wang (2018). End-to-end Active Object Tracking via Reinforcement Learning. In Proceedings of the 35th International Conference on Machine Learning, 3286--3295."},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the 32th International Conference on Machine Learning, 2285--2294","author":"Chen W.","year":"2015","unstructured":"W. Chen , J. T. Wilson , S. Tyree , K. Q. Weinberger , and Y. Chen ( 2015 ). Compressing neural networks with the hashing trick . In Proceedings of the 32th International Conference on Machine Learning, 2285--2294 . W. Chen, J. T. Wilson, S. Tyree, K. Q. Weinberger, and Y. Chen (2015). Compressing neural networks with the hashing trick. In Proceedings of the 32th International Conference on Machine Learning, 2285--2294."},{"key":"e_1_3_2_1_12_1","volume-title":"Advances in neural information processing systems, 5784--5793","author":"Han S.","year":"2015","unstructured":"S. Han , J. Pool , J. Tran , and W. Dally ( 2015 ). Learning both weights and connections for efficient neural network . In Advances in neural information processing systems, 5784--5793 S. Han, J. Pool, J. Tran, and W. Dally (2015). Learning both weights and connections for efficient neural network. In Advances in neural information processing systems, 5784--5793"},{"key":"e_1_3_2_1_13_1","volume-title":"Projection Net: Learning efficient on-device deep networks using neural projections. https:\/\/arxiv.org\/abs\/1708.00630","author":"Ravi S.","year":"2017","unstructured":"S. Ravi ( 2017 ). Projection Net: Learning efficient on-device deep networks using neural projections. https:\/\/arxiv.org\/abs\/1708.00630 . S. Ravi (2017). Projection Net: Learning efficient on-device deep networks using neural projections. https:\/\/arxiv.org\/abs\/1708.00630."},{"key":"e_1_3_2_1_14_1","volume-title":"Distilling the knowledge in a neural network, https:\/\/arxiv.org\/abs\/1503.02531","author":"Hinton G.E.","year":"2015","unstructured":"G.E. Hinton , Geoffrey, and J. Dean ( 2015 ). Distilling the knowledge in a neural network, https:\/\/arxiv.org\/abs\/1503.02531 G.E.Hinton, Geoffrey, and J. Dean (2015). Distilling the knowledge in a neural network, https:\/\/arxiv.org\/abs\/1503.02531"},{"key":"e_1_3_2_1_15_1","volume-title":"Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to +1 or -1. https:\/\/arxiv.org\/abs\/1602.02830","author":"Courbariaux M.","year":"2016","unstructured":"M. Courbariaux , I. Hubara , D. Soudry , and Y. Bengio ( 2016 ). Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to +1 or -1. https:\/\/arxiv.org\/abs\/1602.02830 M. Courbariaux, I. Hubara, D. Soudry, and Y. Bengio (2016). Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to +1 or -1. https:\/\/arxiv.org\/abs\/1602.02830"},{"key":"e_1_3_2_1_16_1","volume-title":"Advances in neural information processing systems, 5647--5657","author":"Courbariaux M.","year":"2015","unstructured":"M. Courbariaux , Y. Bengio and J. P. David ( 2015 ). Binary Connect: Training deep neural networks with binary weights during propagations . In Advances in neural information processing systems, 5647--5657 M. Courbariaux, Y. Bengio and J. P. David (2015). Binary Connect: Training deep neural networks with binary weights during propagations. In Advances in neural information processing systems, 5647--5657"},{"key":"e_1_3_2_1_17_1","volume-title":"Citeseer.","author":"Krizhevsky A.","year":"2009","unstructured":"A. Krizhevsky , G.E. Hinton ( 2009 ). Learning multiple layers of features from tiny images. Tech. rep ., Citeseer. A. Krizhevsky, G.E. Hinton (2009). Learning multiple layers of features from tiny images. Tech. rep., Citeseer."},{"key":"e_1_3_2_1_18_1","volume-title":"Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. https:\/\/arxiv.org\/abs\/1606.06160","author":"Zhou S.","year":"2016","unstructured":"S. Zhou , Z. Ni , and Y. Zou ( 2016 ). Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. https:\/\/arxiv.org\/abs\/1606.06160 S. Zhou, Z. Ni, and Y. Zou (2016). Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. https:\/\/arxiv.org\/abs\/1606.06160"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"volume-title":"SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks. In The IEEE Conference on Computer Vision and Pattern Recognition, 4300--4309","author":"Faraone J.","key":"e_1_3_2_1_20_1","unstructured":"J. Faraone , N. Fraser , and Philip H.W . Leong (2018) . SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks. In The IEEE Conference on Computer Vision and Pattern Recognition, 4300--4309 . J. Faraone, N. Fraser, and Philip H.W. Leong (2018). SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks. In The IEEE Conference on Computer Vision and Pattern Recognition, 4300--4309."},{"key":"e_1_3_2_1_21_1","volume-title":"Advances in neural information processing systems, 6638--6648.","author":"Zhao C.","year":"2017","unstructured":"Xiaofan. Lin, C. Zhao , and W. Pan ( 2017 ). Towards Accurate Binary Convolutional Neural Network . In Advances in neural information processing systems, 6638--6648. Xiaofan. Lin, C. Zhao, and W. Pan (2017). Towards Accurate Binary Convolutional Neural Network. In Advances in neural information processing systems, 6638--6648."},{"key":"e_1_3_2_1_22_1","volume-title":"Estimating or propagating gradients through stochastic neurons for conditional computation. https:\/\/arxiv.org\/abs\/1308.3432","author":"Bengio Y.","year":"2013","unstructured":"Y. Bengio , N. L\u00e9onard , and A. C. Courville ( 2013 ). Estimating or propagating gradients through stochastic neurons for conditional computation. https:\/\/arxiv.org\/abs\/1308.3432 Y. Bengio, N. L\u00e9onard, and A. C. Courville (2013). Estimating or propagating gradients through stochastic neurons for conditional computation. https:\/\/arxiv.org\/abs\/1308.3432"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_1_24_1","volume-title":"NIPS Workshop on Deep Learning and Unsupervised Feature Learning","author":"Netzer Y.","year":"2011","unstructured":"Y. Netzer , T. Wang , A. Coates , A. Bissacco , and A.Y. Ng ( 2011 ). Reading digits in natural images with unsupervised feature learning . In NIPS Workshop on Deep Learning and Unsupervised Feature Learning , 2011, 5--7. Y. Netzer, T. Wang, A. Coates, A. Bissacco, and A.Y. Ng (2011). Reading digits in natural images with unsupervised feature learning. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011, 5--7."},{"key":"e_1_3_2_1_25_1","volume-title":"Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm. https:\/\/arxiv.org\/abs\/1808.00278","author":"Liu Z.H.","year":"2018","unstructured":"Z.H. Liu , B. Y. Wu , and Kwang-Ting Cheng ( 2018 ). Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm. https:\/\/arxiv.org\/abs\/1808.00278 Z.H. Liu, B. Y. Wu, and Kwang-Ting Cheng (2018). Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm. https:\/\/arxiv.org\/abs\/1808.00278"},{"key":"e_1_3_2_1_26_1","volume-title":"Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2625--2632","author":"Tang W.","year":"2017","unstructured":"W. Tang , G. Hua , and L. Wang ( 2017 ). How to Train a Compact Binary Neural Network with High Accuracy . In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2625--2632 . W. Tang, G. Hua, and L. Wang (2017). How to Train a Compact Binary Neural Network with High Accuracy. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2625--2632."},{"key":"e_1_3_2_1_27_1","volume-title":"the 3rd International Conference for Learning Representations.","author":"Kingma D.","year":"2014","unstructured":"D. Kingma , and J. Ba ( 2014 ). Adam: a method for stochastic optimization . In the 3rd International Conference for Learning Representations. D. Kingma, and J. Ba (2014). Adam: a method for stochastic optimization. In the 3rd International Conference for Learning Representations."},{"key":"e_1_3_2_1_28_1","volume-title":"Advances in neural information processing systems, 4937--4946.","author":"Zhang Rie","year":"2013","unstructured":"Johnson. Rie and T. Zhang ( 2013 ). Accelerating Stochastic Gradient Descent using Predictive Variance Reduction . In Advances in neural information processing systems, 4937--4946. Johnson. Rie and T. Zhang (2013). Accelerating Stochastic Gradient Descent using Predictive Variance Reduction. In Advances in neural information processing systems, 4937--4946."},{"key":"e_1_3_2_1_29_1","volume-title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. https:\/\/arxiv.org\/abs\/1502.03167","author":"Ioffe S.","year":"2015","unstructured":"S. Ioffe , C. Szegedy ( 2015 ). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. https:\/\/arxiv.org\/abs\/1502.03167 , S. Ioffe, C. Szegedy (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. https:\/\/arxiv.org\/abs\/1502.03167,"},{"key":"e_1_3_2_1_30_1","volume-title":"Yu et al","author":"Agarwal A.","year":"2015","unstructured":"A. Agarwal , P. Barham , E. Brevdo , and Y. Yu et al ( 2015 ). Tensor-Flow : Large-scale machine learning on heterogeneous systems. https:\/\/arxiv.org\/abs\/1603.04467 A. Agarwal, P. Barham, E. Brevdo, and Y. Yu et al (2015). Tensor-Flow: Large-scale machine learning on heterogeneous systems. https:\/\/arxiv.org\/abs\/1603.04467"}],"event":{"name":"ACAI 2019: 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence","sponsor":["Chinese Univ. of Hong Kong Chinese University of Hong Kong"],"location":"Sanya China","acronym":"ACAI 2019"},"container-title":["Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3377713.3377721","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3377713.3377721","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:23:55Z","timestamp":1750202635000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3377713.3377721"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,20]]},"references-count":30,"alternative-id":["10.1145\/3377713.3377721","10.1145\/3377713"],"URL":"https:\/\/doi.org\/10.1145\/3377713.3377721","relation":{},"subject":[],"published":{"date-parts":[[2019,12,20]]},"assertion":[{"value":"2020-02-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}