{"id":"https://openalex.org/W1902041153","doi":"https://doi.org/10.1109/cvpr.2015.7298809","title":"Efficient and accurate approximations of nonlinear convolutional networks","display_name":"Efficient and accurate approximations of nonlinear convolutional networks","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1902041153","doi":"https://doi.org/10.1109/cvpr.2015.7298809","mag":"1902041153"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298809","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298809","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100362465","display_name":"Xiangyu Zhang","orcid":"https://orcid.org/0000-0003-2138-4608"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiangyu Zhang","raw_affiliation_strings":["Xi\u2019 an Jiaotong University","Xi'an Jiaotong Univ.  (China)"],"affiliations":[{"raw_affiliation_string":"Xi\u2019 an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"Xi'an Jiaotong Univ.  (China)","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103277719","display_name":"Jianhua Zou","orcid":"https://orcid.org/0000-0003-1632-4758"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhua Zou","raw_affiliation_strings":["Xi\u2019 an Jiaotong University","Xi'an Jiaotong Univ.  (China)"],"affiliations":[{"raw_affiliation_string":"Xi\u2019 an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"Xi'an Jiaotong Univ.  (China)","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101977297","display_name":"Ming Xiang","orcid":"https://orcid.org/0000-0002-5512-9800"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Ming","raw_affiliation_strings":["Xi\u2019 an Jiaotong University","Xi'an Jiaotong Univ.  (China)"],"affiliations":[{"raw_affiliation_string":"Xi\u2019 an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"Xi'an Jiaotong Univ.  (China)","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100700361","display_name":"Kaiming He","orcid":"https://orcid.org/0000-0001-7318-9658"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]},{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Kaiming He","raw_affiliation_strings":["Microsoft Research","Microsoft Research,USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]},{"raw_affiliation_string":"Microsoft Research,USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101425421","display_name":"Jian Sun","orcid":"https://orcid.org/0000-0001-6270-2698"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]},{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Jian Sun","raw_affiliation_strings":["Microsoft Research","Microsoft Research,USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]},{"raw_affiliation_string":"Microsoft Research,USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100362465"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":18.3438,"has_fulltext":false,"cited_by_count":290,"citation_normalized_percentile":{"value":0.99431921,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1984","last_page":"1992"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.8564862012863159},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7479602098464966},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6979731321334839},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.6626150608062744},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6321391463279724},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5820053219795227},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5312435626983643},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.47847768664360046},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3729926347732544},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36979925632476807},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1810230016708374},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.1384097933769226}],"concepts":[{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.8564862012863159},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7479602098464966},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6979731321334839},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.6626150608062744},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6321391463279724},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5820053219795227},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5312435626983643},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.47847768664360046},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3729926347732544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36979925632476807},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1810230016708374},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.1384097933769226},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2015.7298809","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298809","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.764.165","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.764.165","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1411.4229.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.845.95","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.845.95","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhang_Efficient_and_Accurate_2015_CVPR_paper.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W587794757","https://openalex.org/W1507506748","https://openalex.org/W1559215823","https://openalex.org/W1665214252","https://openalex.org/W1686810756","https://openalex.org/W1709548961","https://openalex.org/W1996901117","https://openalex.org/W2027922120","https://openalex.org/W2066486477","https://openalex.org/W2097117768","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2109255472","https://openalex.org/W2117539524","https://openalex.org/W2147800946","https://openalex.org/W2163605009","https://openalex.org/W2165507622","https://openalex.org/W2167215970","https://openalex.org/W2179352600","https://openalex.org/W2206858481","https://openalex.org/W2950179405","https://openalex.org/W2950248853","https://openalex.org/W2962835968","https://openalex.org/W2963173190","https://openalex.org/W2963542991","https://openalex.org/W2963911037","https://openalex.org/W4241492945","https://openalex.org/W6617368339","https://openalex.org/W6629368666","https://openalex.org/W6637242042","https://openalex.org/W6637373629","https://openalex.org/W6637616945","https://openalex.org/W6638444622","https://openalex.org/W6648737282","https://openalex.org/W6649495467","https://openalex.org/W6674914833","https://openalex.org/W6676297131","https://openalex.org/W6676338569","https://openalex.org/W6677651945","https://openalex.org/W6684191040","https://openalex.org/W6684563725","https://openalex.org/W6688059459"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2027972911","https://openalex.org/W2146343568","https://openalex.org/W2013643406","https://openalex.org/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W3046370962"],"abstract_inverted_index":{"This":[0],"paper":[1],"aims":[2],"to":[3,45,51,62],"accelerate":[4],"the":[5,30,37,41,53,75,99,117],"test-time":[6],"computation":[7],"of":[8,40,55,87],"deep":[9],"convolutional":[10],"neural":[11],"networks":[12],"(CNNs).":[13],"Unlike":[14],"existing":[15],"methods":[16],"that":[17],"are":[18,81],"designed":[19],"for":[20,73,96],"approximating":[21],"linear":[22,25],"filters":[23],"or":[24],"responses,":[26,43],"our":[27],"method":[28],"takes":[29],"nonlinear":[31,42,65],"units":[32],"into":[33],"account.":[34],"We":[35,57],"minimize":[36],"reconstruction":[38],"error":[39,77,101],"subject":[44],"a":[46,92,112],"low-rank":[47],"constraint":[48],"which":[49],"helps":[50],"reduce":[52],"complexity":[54],"filters.":[56],"develop":[58],"an":[59],"effective":[60],"solution":[61],"this":[63],"constrained":[64],"optimization":[66],"problem.":[67],"An":[68],"algorithm":[69],"is":[70,89,103,121],"also":[71],"presented":[72],"reducing":[74],"accumulated":[76],"when":[78],"multiple":[79],"layers":[80],"approximated.":[82],"A":[83],"whole-model":[84],"speedup":[85],"ratio":[86],"4\u00d7":[88],"demonstrated":[90],"on":[91],"large":[93],"network":[94],"trained":[95],"ImageNet,":[97],"while":[98],"top-5":[100],"rate":[102],"only":[104],"increased":[105],"by":[106],"0.9%.":[107],"Our":[108],"accelerated":[109],"model":[110],"has":[111],"comparably":[113],"fast":[114],"speed":[115],"as":[116],"\u201cAlexNet\u201d":[118],"[11],":[119],"but":[120],"4.7%":[122],"more":[123],"accurate.":[124]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":36},{"year":2020,"cited_by_count":50},{"year":2019,"cited_by_count":45},{"year":2018,"cited_by_count":38},{"year":2017,"cited_by_count":35},{"year":2016,"cited_by_count":19},{"year":2015,"cited_by_count":6}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
