{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:53:07Z","timestamp":1764960787801,"version":"3.46.0"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076016"],"award-info":[{"award-number":["62076016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Shandong Province to Dianmin Sun","award":["2019JZZY011101"],"award-info":[{"award-number":["2019JZZY011101"]}]},{"name":"Shenzhen Science and Technology Program","award":["KQTD2016112515134654"],"award-info":[{"award-number":["KQTD2016112515134654"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1109\/tnnls.2021.3060830","type":"journal-article","created":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T16:10:48Z","timestamp":1615306248000},"page":"3916-3929","source":"Crossref","is-referenced-by-count":6,"title":["Modulated Convolutional Networks"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7396-6218","authenticated-orcid":false,"given":"Baochang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"}]},{"given":"Runqi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"}]},{"given":"Xiaodi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4361-956X","authenticated-orcid":false,"given":"Jungong","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Prifysgol Aberystwyth University, Aberystwyth, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9163-2932","authenticated-orcid":false,"given":"Rongrong","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, China"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1137","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume-title":"Proc. Conf. Workshop Neural Inf. Process. Syst.","author":"Ren"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.672"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2819978"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref5","first-page":"111","article-title":"A theoretical analysis of feature pooling in visual recognition","volume-title":"Proc. 27th Int. Conf. Mach. Learn.","author":"Boureau"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.527"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"ref8","article-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or \u22121","volume-title":"arXiv:1602.02830","author":"Courbariaux","year":"2016"},{"key":"ref9","first-page":"3123","article-title":"Binaryconnect: Training deep neural networks with binary weights during propagations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Courbariaux"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.456"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0880-y"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00094"},{"article-title":"Pruning filters for efficient convnets","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Li","key":"ref13"},{"article-title":"Learning both weights and connections for efficient neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Han","key":"ref14"},{"key":"ref15","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-58536-5_38","article-title":"EagleEye: Fast sub-net evaluation for efficient neural network pruning","volume-title":"arXiv:2007.02491","author":"Li","year":"2020"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00982"},{"article-title":"Training and inference with integers in deep neural networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wu","key":"ref17"},{"article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Han","key":"ref18"},{"key":"ref19","first-page":"1","article-title":"Analysis of quantized models","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Hou"},{"key":"ref20","article-title":"Understanding straight-through estimator in training activation quantized neural nets","volume-title":"arXiv:1903.05662","author":"Yin","year":"2019"},{"key":"ref21","article-title":"Exploiting linear structure within convolutional networks for efficient evaluation","volume-title":"Neural Information Processing Systems","author":"Denton","year":"2014"},{"article-title":"Convolutional neural networks with low-rank regularization","volume-title":"Proc. ICLR","author":"Tai","key":"ref22"},{"key":"ref23","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-58452-8_31","article-title":"Spatially adaptive inference with stochastic feature sampling and interpolation","volume-title":"arXiv:2003.08866","author":"Xie","year":"2020"},{"article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Howard","key":"ref24"},{"key":"ref25","article-title":"ShuffleNet: An extremely efficient convolutional neural network for mobile devices","volume-title":"arXiv:1707.01083","author":"Zhang","year":"2017"},{"article-title":"SqueezeNet: AlexNet-level accuracy with 50\u00d7 fewer parameters and <0.5MB model size","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Iandola","key":"ref26"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6102"},{"key":"ref28","first-page":"2285","article-title":"Compressing neural networks with the hashing trick","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157329"},{"article-title":"Dynamic network surgery for efficient DNNs","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Guo","key":"ref30"},{"key":"ref31","article-title":"Training skinny deep neural networks with iterative hard thresholding methods","volume-title":"arXiv:1607.05423","author":"Jin","year":"2016"},{"key":"ref32","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"arXiv:1409.1556","author":"Simonyan","year":"2014"},{"key":"ref33","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Alex"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00292"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00026"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.03.026"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref41"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.2118\/18761-MS"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"volume-title":"Cs231n Project Report\u2014Tiny Imagenet Challenge","year":"2015","author":"Banerjee","key":"ref44"},{"key":"ref45","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","volume-title":"arXiv:1207.0580","author":"Hinton","year":"2012"},{"key":"ref46","article-title":"ADADELTA: An adaptive learning rate method","volume-title":"arXiv:1212.5701","author":"Zeiler","year":"2012"},{"key":"ref47","first-page":"1319","article-title":"Maxout networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Goodfellow"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.4400"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3410338.3412340"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1512.02325"},{"key":"ref54","article-title":"YOLOv3: An incremental improvement","volume-title":"arXiv:1804.02767","author":"Redmon","year":"2018"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref56","article-title":"Objects as points","volume-title":"arXiv:1904.07850","author":"Zhou","year":"2019"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10908444\/09374055.pdf?arnumber=9374055","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:39:11Z","timestamp":1764959951000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9374055\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":56,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2021.3060830","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"type":"print","value":"2162-237X"},{"type":"electronic","value":"2162-2388"}],"subject":[],"published":{"date-parts":[[2025,3]]}}}