{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T22:51:56Z","timestamp":1772751116416,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T00:00:00Z","timestamp":1603324800000},"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":[[2020,10,22]]},"DOI":"10.1145\/3432291.3432293","type":"proceedings-article","created":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T22:41:12Z","timestamp":1607121672000},"page":"21-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["An Automatic Scheme for Optimizing the Size of Deep Networks"],"prefix":"10.1145","author":[{"given":"Wenting","family":"Ma","sequence":"first","affiliation":[{"name":"China Mobile Research Institute, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhipeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Mobile Research Institute, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingqing","family":"Xu","sequence":"additional","affiliation":[{"name":"China Mobile Research Institute, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wai","family":"Chen","sequence":"additional","affiliation":[{"name":"China Mobile Research Institute, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,12,4]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Krizhevsky A. Sutskever I. and Hinton G. E.. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.  Krizhevsky A. Sutskever I. and Hinton G. E.. 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","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2017.2761740"},{"key":"e_1_3_2_1_3_1","volume-title":"Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229.","author":"Sermanet P.","year":"2013","unstructured":"Sermanet , P. , Eigen , D. , Zhang , X. , Mathieu , M. , Fergus , R. , and LeCun , Y.. 2013 . Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y.. 2013. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229."},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of the 13th European Conference Computer Vision and Pattern Recognition","author":"Matthew D.","year":"2014","unstructured":"Matthew , D. , and Fergus , R .. 2014. Visualizing and understanding convolutional neural networks . In Proceedings of the 13th European Conference Computer Vision and Pattern Recognition , Zurich, Switzerland. ( Sep. 2014 ), 6--12. Matthew, D., and Fergus, R.. 2014. Visualizing and understanding convolutional neural networks. In Proceedings of the 13th European Conference Computer Vision and Pattern Recognition, Zurich, Switzerland. (Sep. 2014), 6--12."},{"key":"e_1_3_2_1_5_1","unstructured":"Simonyan K. and Zisserman A.. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.  Simonyan K. and Zisserman A.. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556."},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778","author":"He K.","unstructured":"He , K. , Zhang , X. , Ren , S. , and Sun , J .. 2016. Deep residual learning for image recognition . In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778 . He, K., Zhang, X., Ren, S., and Sun, J.. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778."},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 9349--9358","author":"Keshari R.","unstructured":"Keshari , R. , Vatsa , M. , Singh , R. , and Noore , A .. 2018. Learning structure and strength of CNN filters for small sample size training . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 9349--9358 . Keshari, R., Vatsa, M., Singh, R., and Noore, A.. 2018. Learning structure and strength of CNN filters for small sample size training. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 9349--9358."},{"key":"e_1_3_2_1_8_1","volume-title":"International Symposium on Intelligent Data Analysis.(Oct.","author":"Soekhoe D.","year":"2016","unstructured":"Soekhoe , D. , Van Der Putten , P. , and Plaat , A .. 2016. On the impact of data set size in transfer learning using deep neural networks . In International Symposium on Intelligent Data Analysis.(Oct. 2016 ), 50--60. Soekhoe, D., Van Der Putten, P., and Plaat, A.. 2016. On the impact of data set size in transfer learning using deep neural networks. In International Symposium on Intelligent Data Analysis.(Oct. 2016), 50--60."},{"key":"e_1_3_2_1_9_1","unstructured":"Denton E. L. Zaremba W. Bruna J. LeCun Y. and Fergus R.. 2014. Exploiting linear structure within convolutional networks for efficient evaluation. In Advances in neural information processing systems.1269--1277.  Denton E. L. Zaremba W. Bruna J. LeCun Y. and Fergus R.. 2014. Exploiting linear structure within convolutional networks for efficient evaluation. In Advances in neural information processing systems.1269--1277."},{"key":"e_1_3_2_1_10_1","volume-title":"International conference on machine learning. 2285--2294","author":"Chen W.","unstructured":"Chen , W. , Wilson , J. , Tyree , S. , Weinberger , K. , and Chen , Y .. 2015. Compressing neural networks with the hashing trick . In International conference on machine learning. 2285--2294 . Chen, W., Wilson, J., Tyree, S., Weinberger, K., and Chen, Y.. 2015. Compressing neural networks with the hashing trick. In International conference on machine learning. 2285--2294."},{"key":"e_1_3_2_1_11_1","unstructured":"Han S. Pool J. Tran J. and Dally W.. 2015. Learning both weights and connections for efficient neural network. In Advances in neural information processing systems. 1135--1143.  Han S. Pool J. Tran J. and Dally W.. 2015. Learning both weights and connections for efficient neural network. In Advances in neural information processing systems. 1135--1143."},{"key":"e_1_3_2_1_12_1","volume-title":"European conference on computer vision. 525--542","author":"Rastegari M.","unstructured":"Rastegari , M. , Ordonez , V. , Redmon , J. , and Farhadi , A .. 2016. Xnor-net: Imagenet classification using binary convolutional neural networks . In European conference on computer vision. 525--542 . Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A.. 2016. Xnor-net: Imagenet classification using binary convolutional neural networks. In European conference on computer vision. 525--542."},{"key":"e_1_3_2_1_13_1","unstructured":"Courbariaux M. Hubara I. Soudry D. El-Yaniv R. and Bengio Y.. 2016. Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830.  Courbariaux M. Hubara I. Soudry D. El-Yaniv R. and Bengio Y.. 2016. Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830."},{"key":"e_1_3_2_1_14_1","volume-title":"L., and Weinberger, K. Q..","author":"Huang G.","year":"2017","unstructured":"Huang , G. , Chen , D. , Li , T. , Wu , F. , Van Der Maaten , L., and Weinberger, K. Q.. 2017 . Multi-scale de nse convolutional networks for efficient prediction. arXiv preprint arXiv:1703.09844, 2. Huang, G., Chen, D., Li, T., Wu, F., Van Der Maaten, L., and Weinberger, K. Q.. 2017. Multi-scale dense convolutional networks for efficient prediction. arXiv preprint arXiv:1703.09844, 2."},{"key":"e_1_3_2_1_15_1","unstructured":"LeCun Y. Denker J. S. and Solla S. A.. 1990. Optimal brain damage. In Advances in neural information processing systems. 598--605.  LeCun Y. Denker J. S. and Solla S. A.. 1990. Optimal brain damage. In Advances in neural information processing systems. 598--605."},{"key":"e_1_3_2_1_16_1","unstructured":"Han S. Mao H. and Dally W. J.. 2015. Deep compression: Compressing deep neural networks with pruning trained quantization and huffman coding. arXiv preprint arXiv:510.00149.  Han S. Mao H. and Dally W. J.. 2015. Deep compression: Compressing deep neural networks with pruning trained quantization and huffman coding. arXiv preprint arXiv:510.00149."},{"key":"e_1_3_2_1_17_1","unstructured":"Han S. Pool J. Tran J. and Dally W.. 2015. Learning both weights and connections for efficient neural network. In Advances in neural information processing systems. 1135--1143.  Han S. Pool J. Tran J. and Dally W.. 2015. Learning both weights and connections for efficient neural network. In Advances in neural information processing systems. 1135--1143."},{"key":"e_1_3_2_1_18_1","unstructured":"Hassibi B. and Stork D. G.. 1993. Second order derivatives for network pruning: Optimal brain surgeon. In Advances in neural information processing systems. 164--171.  Hassibi B. and Stork D. G.. 1993. Second order derivatives for network pruning: Optimal brain surgeon. In Advances in neural information processing systems. 164--171."},{"key":"e_1_3_2_1_19_1","unstructured":"Ba J. and Caruana R.. 2014. Do deep nets really need to be deep?. In Advances in neural information processing systems. 2654--2662.  Ba J. and Caruana R.. 2014. Do deep nets really need to be deep?. In Advances in neural information processing systems. 2654--2662."},{"key":"e_1_3_2_1_20_1","unstructured":"Wen W. Wu C. Wang Y. Chen Y. and Li H.. 2016. Learning structured sparsity in deep neural networks. In Advances in neural information processing systems. 2074--2082.  Wen W. Wu C. Wang Y. Chen Y. and Li H.. 2016. Learning structured sparsity in deep neural networks. In Advances in neural information processing systems. 2074--2082."},{"key":"e_1_3_2_1_21_1","unstructured":"Li H. Kadav A. Durdanovic I. Samet H. and Graf H. P.. 2016. Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710.  Li H. Kadav A. Durdanovic I. Samet H. and Graf H. P.. 2016. Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710."},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the IEEE international conference on computer vision. 1026--1034","author":"He K.","unstructured":"He , K. , Zhang , X. , Ren , S. , and Sun , J .. 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification . In Proceedings of the IEEE international conference on computer vision. 1026--1034 . He, K., Zhang, X., Ren, S., and Sun, J.. 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision. 1026--1034."},{"key":"e_1_3_2_1_23_1","unstructured":"Iandola F. N. Han S. Moskewicz M. W. Ashraf K. Dally W. J. and Keutzer K.. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360.  Iandola F. N. Han S. Moskewicz M. W. Ashraf K. Dally W. J. and Keutzer K.. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360."},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the IEEE International Conference on Computer Vision. 1314--1324","author":"Howard A.","unstructured":"Howard , A. , Sandler , M. , Chu , G. , Chen , L. C. , Chen , B. , Tan , M. , ... and Le , Q. V .. 2019. Searching for mobilenetv3 . In Proceedings of the IEEE International Conference on Computer Vision. 1314--1324 . Howard, A., Sandler, M., Chu, G., Chen, L. C., Chen, B., Tan, M., ... and Le, Q. V.. 2019. Searching for mobilenetv3. In Proceedings of the IEEE International Conference on Computer Vision. 1314--1324."},{"key":"e_1_3_2_1_25_1","unstructured":"Chen L. C. Papandreou G. Schroff F. and Adam H.. 2017. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587.  Chen L. C. Papandreou G. Schroff F. and Adam H.. 2017. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2963635"},{"key":"e_1_3_2_1_27_1","volume-title":"A new look at the statistical model R@identification","author":"Akaike H..","unstructured":"Akaike , H.. 1974. A new look at the statistical model R@identification . IEEE transactions on automatic control, 19, 6, 716--723. Akaike, H.. 1974. A new look at the statistical model R@identification. IEEE transactions on automatic control, 19, 6, 716--723."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02532770"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Schwarz G.. 1978. Estimating the dimension of a model. The annals of statistics 6 2 461--464.  Schwarz G.. 1978. Estimating the dimension of a model. The annals of statistics 6 2 461--464.","DOI":"10.1214\/aos\/1176344136"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/11.2.185"},{"key":"e_1_3_2_1_31_1","volume-title":"Information theory, inference and learning algorithms","author":"MacKay D. J.","unstructured":"MacKay , D. J. , and Mac Kay , D. J.. 2003. Information theory, inference and learning algorithms . Cambridge university press . MacKay, D. J., and Mac Kay, D. J.. 2003. Information theory, inference and learning algorithms. Cambridge university press."},{"key":"e_1_3_2_1_32_1","volume-title":"Stochastic complexity in statistical inquiry. 15, 188","author":"Rissanen J..","unstructured":"Rissanen , J.. 1989. Stochastic complexity in statistical inquiry. 15, 188 . Singapore : World scientific. Rissanen, J.. 1989. Stochastic complexity in statistical inquiry. 15, 188. Singapore: World scientific."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/18.720554"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753168398"},{"key":"e_1_3_2_1_35_1","unstructured":"Krizhevsky A. and Hinton G.. 2009. Learning multiple layers of features from tiny images. Handbook of Systemic Autoimmune Diseases 4.  Krizhevsky A. and Hinton G.. 2009. Learning multiple layers of features from tiny images. Handbook of Systemic Autoimmune Diseases 4."},{"key":"e_1_3_2_1_36_1","unstructured":"Ioffe S. and Szegedy C.. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167.  Ioffe S. and Szegedy C.. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167."},{"key":"e_1_3_2_1_37_1","volume-title":"Proceedings of the 27th International Conference on Machine Learning","author":"Nair V.","year":"2010","unstructured":"Nair , V. , and Hinton , G. E .. 2010. Rectified linear units improve restricted boltzmann machines . In Proceedings of the 27th International Conference on Machine Learning , June 21-24, 2010 , Haifa, Israel. Nair, V., and Hinton, G. E.. 2010. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning, June 21-24, 2010, Haifa, Israel."},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the IEEE International Conference on Computer Vision. 2736--2744","author":"Liu Z.","unstructured":"Liu , Z. , Li , J. , Shen , Z. , Huang , G. , Yan , S. , and Zhang , C .. 2017. Learning efficient convolutional networks through network slimming . In Proceedings of the IEEE International Conference on Computer Vision. 2736--2744 . Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., and Zhang, C.. 2017. Learning efficient convolutional networks through network slimming. In Proceedings of the IEEE International Conference on Computer Vision. 2736--2744."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"}],"event":{"name":"SPML 2020: 2020 3rd International Conference on Signal Processing and Machine Learning","location":"Beijing China","acronym":"SPML 2020"},"container-title":["Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3432291.3432293","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3432291.3432293","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:10Z","timestamp":1750193230000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3432291.3432293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,22]]},"references-count":39,"alternative-id":["10.1145\/3432291.3432293","10.1145\/3432291"],"URL":"https:\/\/doi.org\/10.1145\/3432291.3432293","relation":{},"subject":[],"published":{"date-parts":[[2020,10,22]]},"assertion":[{"value":"2020-12-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}