{"id":"https://openalex.org/W3202230044","doi":"https://doi.org/10.1109/tpami.2021.3117908","title":"Gradient Matters: Designing Binarized Neural Networks via Enhanced Information-Flow","display_name":"Gradient Matters: Designing Binarized Neural Networks via Enhanced Information-Flow","publication_year":2021,"publication_date":"2021-10-07","ids":{"openalex":"https://openalex.org/W3202230044","doi":"https://doi.org/10.1109/tpami.2021.3117908","mag":"3202230044","pmid":"https://pubmed.ncbi.nlm.nih.gov/34613908"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2021.3117908","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2021.3117908","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5100341321","display_name":"Qi Wang","orcid":"https://orcid.org/0000-0002-7028-4956"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qi Wang","raw_affiliation_strings":["School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043579361","display_name":"Nianhui Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nianhui Guo","raw_affiliation_strings":["School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022524675","display_name":"Zhitong Xiong","orcid":"https://orcid.org/0000-0002-3953-585X"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhitong Xiong","raw_affiliation_strings":["School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042585846","display_name":"Zeping Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeping Yin","raw_affiliation_strings":["School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106943753","display_name":"Xuelong Li","orcid":"https://orcid.org/0000-0003-2924-946X"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuelong Li","raw_affiliation_strings":["School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100341321"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.4845,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.65516012,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"44","issue":"11","first_page":"7551","last_page":"7562"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6921632289886475},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6703855395317078},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6424510478973389},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5939034819602966},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5828880667686462},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5792874693870544},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5447734594345093},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5063213109970093},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49922943115234375},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4956156015396118},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4423134922981262},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.43338724970817566},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4317505359649658},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.412153959274292},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2923470735549927},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11658552289009094},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.10108253359794617}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6921632289886475},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6703855395317078},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6424510478973389},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5939034819602966},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5828880667686462},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5792874693870544},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5447734594345093},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5063213109970093},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49922943115234375},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4956156015396118},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4423134922981262},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.43338724970817566},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4317505359649658},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.412153959274292},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2923470735549927},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11658552289009094},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.10108253359794617},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2021.3117908","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2021.3117908","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:34613908","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34613908","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4820425477","display_name":null,"funder_award_id":"61871470","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8046557032","display_name":null,"funder_award_id":"61773316","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W2108598243","https://openalex.org/W2242818861","https://openalex.org/W2300242332","https://openalex.org/W2319920447","https://openalex.org/W2327501763","https://openalex.org/W2412782625","https://openalex.org/W2469490737","https://openalex.org/W2524428287","https://openalex.org/W2547875792","https://openalex.org/W2553303224","https://openalex.org/W2560017826","https://openalex.org/W2586654419","https://openalex.org/W2746553466","https://openalex.org/W2748513770","https://openalex.org/W2751477244","https://openalex.org/W2763421725","https://openalex.org/W2785961331","https://openalex.org/W2884150179","https://openalex.org/W2887447938","https://openalex.org/W2890311316","https://openalex.org/W2897818259","https://openalex.org/W2908314593","https://openalex.org/W2914381000","https://openalex.org/W2952716587","https://openalex.org/W2954048742","https://openalex.org/W2957777252","https://openalex.org/W2962703323","https://openalex.org/W2962824709","https://openalex.org/W2964195914","https://openalex.org/W2964203871","https://openalex.org/W2964280870","https://openalex.org/W2966671006","https://openalex.org/W2970691381","https://openalex.org/W4287824105","https://openalex.org/W6639703010","https://openalex.org/W6684089856","https://openalex.org/W6690026940","https://openalex.org/W6692846177","https://openalex.org/W6700264148","https://openalex.org/W6720242923","https://openalex.org/W6727208969","https://openalex.org/W6729448088","https://openalex.org/W6729956949","https://openalex.org/W6730047919","https://openalex.org/W6743495212","https://openalex.org/W6743755670","https://openalex.org/W6744700018","https://openalex.org/W6745245109","https://openalex.org/W6748163181","https://openalex.org/W6748522178","https://openalex.org/W6748713063","https://openalex.org/W6755312952","https://openalex.org/W6755501315","https://openalex.org/W6757713054","https://openalex.org/W6758743346","https://openalex.org/W6772326514","https://openalex.org/W6864424756"],"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/W2891987081","https://openalex.org/W3157543420"],"abstract_inverted_index":{"Binarized":[0],"neural":[1,48,93,180],"networks":[2,49,181],"(BNNs)":[3],"have":[4],"drawn":[5],"significant":[6],"attention":[7],"in":[8,15,75,110,127],"recent":[9],"years,":[10],"owing":[11],"to":[12,184],"great":[13],"potential":[14],"reducing":[16],"computation":[17],"and":[18,33,46,118,197,208],"storage":[19],"consumption.":[20],"While":[21],"it":[22],"is":[23,166],"attractive,":[24],"traditional":[25],"BNNs":[26,45,95,144,171,211],"usually":[27],"suffer":[28],"from":[29,61,145,172,182],"slow":[30,107],"convergence":[31,108,206],"speed":[32,109,207],"dramatical":[34],"accuracy-degradation":[35],"on":[36,99,192],"large-scale":[37,100],"classification":[38,102],"datasets.":[39],"To":[40],"minimize":[41],"the":[42,62,73,76,89,106,114,135,146,154,173],"gap":[43],"between":[44],"deep":[47],"(DNNs),":[50],"we":[51,79,112],"propose":[52,80,119],"a":[53,120,158,213],"new":[54],"framework":[55],"of":[56,64,91],"designing":[57],"BNNs,":[58,111,202],"dubbed":[59,164],"Hyper-BinaryNet,":[60],"aspect":[63],"enhanced":[65],"information-flow.":[66],"Our":[67],"contributions":[68],"are":[69],"threefold:":[70],"1)":[71],"Considering":[72,105,153],"capacity-limitation":[74],"backward":[77],"pass,":[78],"an":[81],"1-bit":[82],"convolution":[83],"module":[84],"named":[85],"HyperConv.":[86],"By":[87,124],"exploiting":[88],"capacity":[90],"auxiliary":[92],"networks,":[94],"gain":[96],"better":[97],"performance":[98],"image":[101],"task.":[103],"2)":[104],"rethink":[113],"gradient":[115,136,147,160],"accumulation":[116,122],"mechanism":[117],"hyper":[121],"technique.":[123],"accumulating":[125],"gradients":[126],"multiple":[128],"variables":[129],"rather":[130],"than":[131],"one":[132],"as":[133],"before,":[134],"paths":[137],"for":[138],"each":[139],"weight":[140],"increase,":[141],"which":[142],"escapes":[143],"bottleneck":[148],"problem":[149],"during":[150],"training.":[151],"3)":[152],"ill-posed":[155],"optimization":[156,175],"problem,":[157],"novel":[159],"estimation":[161],"warmup":[162],"strategy,":[163],"STE-Warmup,":[165],"developed.":[167],"This":[168],"strategy":[169],"prevents":[170],"unstable":[174],"process":[176],"by":[177,212],"progressively":[178],"transferring":[179],"32-bit":[183],"1-bit.":[185],"We":[186],"conduct":[187],"evaluations":[188],"with":[189,200],"variant":[190],"architectures":[191],"three":[193],"public":[194],"datasets:":[195],"CIFAR-10/100":[196],"ImageNet.":[198],"Compared":[199],"state-of-the-art":[201],"Hyper-BinaryNet":[203],"shows":[204],"faster":[205],"outperforms":[209],"existing":[210],"large":[214],"margin.":[215]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}