{"id":"https://openalex.org/W4206550256","doi":"https://doi.org/10.14778/3485450.3485451","title":"ANN softmax","display_name":"ANN softmax","publication_year":2021,"publication_date":"2021-09-01","ids":{"openalex":"https://openalex.org/W4206550256","doi":"https://doi.org/10.14778/3485450.3485451"},"language":"en","primary_location":{"id":"doi:10.14778/3485450.3485451","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3485450.3485451","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},"type":"article","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/A5103326213","display_name":"Kang Zhao","orcid":"https://orcid.org/0009-0000-3053-3965"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kang Zhao","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027740881","display_name":"Liuyihan Song","orcid":"https://orcid.org/0000-0002-8607-0119"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liuyihan Song","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059194274","display_name":"Yingya Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingya Zhang","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100667153","display_name":"Pan Pan","orcid":"https://orcid.org/0000-0001-5828-0234"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pan Pan","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110763379","display_name":"Yinghui Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinghui Xu","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069394608","display_name":"Rong Jin","orcid":"https://orcid.org/0000-0002-8797-4646"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Jin","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I45928872"],"apc_list":null,"apc_paid":null,"fwci":0.4711,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.6570755,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"15","issue":"1","first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9984999895095825,"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/T11448","display_name":"Face recognition and analysis","score":0.998199999332428,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.8675600290298462},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8066766262054443},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5009288787841797},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4916396737098694},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4705793261528015},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.45252835750579834},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3298798203468323},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3283146619796753},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32248222827911377},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.30530035495758057},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20891955494880676}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.8675600290298462},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8066766262054443},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5009288787841797},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4916396737098694},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4705793261528015},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.45252835750579834},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3298798203468323},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3283146619796753},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32248222827911377},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30530035495758057},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20891955494880676}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14778/3485450.3485451","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3485450.3485451","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1971353978","https://openalex.org/W2037046020","https://openalex.org/W2095779006","https://openalex.org/W2100714283","https://openalex.org/W2108598243","https://openalex.org/W2124509324","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2404498690","https://openalex.org/W2663800299","https://openalex.org/W2797054769","https://openalex.org/W2799167061","https://openalex.org/W2805874993","https://openalex.org/W2808965910","https://openalex.org/W2892284455","https://openalex.org/W2962898354","https://openalex.org/W2963671154","https://openalex.org/W2969985801","https://openalex.org/W2970139027","https://openalex.org/W2972502338","https://openalex.org/W2981467700","https://openalex.org/W2986277806","https://openalex.org/W2998752879","https://openalex.org/W3034303554","https://openalex.org/W3081146346","https://openalex.org/W3086105743","https://openalex.org/W3105233790","https://openalex.org/W4205922070","https://openalex.org/W6629956336","https://openalex.org/W6713134421"],"related_works":["https://openalex.org/W3107204728","https://openalex.org/W4287591324","https://openalex.org/W4226420367","https://openalex.org/W2980176872","https://openalex.org/W2962876041","https://openalex.org/W3090555870","https://openalex.org/W3108503355","https://openalex.org/W2249953602","https://openalex.org/W4323060069","https://openalex.org/W2932872266"],"abstract_inverted_index":{"Thanks":[0],"to":[1,34,43,54,72,83,135,146,184,236,337],"the":[2,7,55,62,74,94,99,110,127,151,160,186,192,210,227,239,265,291,307,320],"popularity":[3],"of":[4,9,31,50,70,90,119,129,188,194,212,322,326],"GPU":[5,302],"and":[6,14,25,58,250,252,281],"growth":[8],"its":[10],"computational":[11],"power,":[12],"more":[13,15,309],"deep":[16,46],"learning":[17],"tasks,":[18],"such":[19,108],"as":[20,109,268],"face":[21],"recognition,":[22],"image":[23],"retrieval":[24,117],"word":[26],"embedding,":[27],"can":[28,199,305,334],"take":[29],"advantage":[30],"extreme":[32],"classification":[33],"improve":[35,185],"accuracy.":[36],"However,":[37],"it":[38,198],"remains":[39],"a":[40,45,67,165,223,300,316,324],"big":[41],"challenge":[42],"train":[44],"model":[47],"with":[48,137,180,284,315],"millions":[49],"classes":[51,71,76,101,214,241,329],"efficiently":[52],"due":[53],"huge":[56],"memory":[57],"computation":[59],"consumption":[60],"in":[61,126,156,172,203,299],"last":[63],"layer.":[64],"By":[65],"sampling":[66,114],"small":[68],"set":[69],"avoid":[73],"total":[75],"calculation,":[77],"sampling-based":[78,167],"approaches":[79],"have":[80],"been":[81],"proved":[82],"be":[84,200,335],"an":[85],"effective":[86],"solution.":[87],"But":[88],"most":[89],"them":[91],"suffer":[92],"from":[93],"following":[95],"two":[96,246],"issues:":[97],"i)":[98],"important":[100,189,213],"are":[102,216],"ignored":[103],"or":[104,116],"only":[105,285],"partly":[106],"sampled,":[107],"methods":[111],"using":[112],"random":[113],"scheme":[115],"techniques":[118],"low":[120],"recall":[121,187],"(e.g.,":[122,255],"locality-sensitive":[123],"hashing),":[124],"resulting":[125],"degradation":[128],"accuracy;":[130],"ii)":[131],"inefficient":[132],"implementation":[133],"owing":[134],"incompatibility":[136],"GPU,":[138],"like":[139],"selective":[140],"softmax.":[141],"It":[142],"uses":[143],"hashing":[144],"forest":[145],"help":[147,193],"select":[148],"classes,":[149,288],"but":[150],"search":[152],"process":[153],"is":[154],"implemented":[155],"CPU.":[157],"To":[158],"address":[159],"above":[161],"issues,":[162],"we":[163,176,208,231,295],"propose":[164],"new":[166],"softmax":[168],"called":[169],"ANN":[170,297],"Softmax":[171,270],"this":[173],"paper.":[174],"Specifically,":[175],"employ":[177],"binary":[178],"quantization":[179],"inverted":[181],"file":[182],"system":[183],"classes.":[190],"With":[191],"dedicated":[195],"kernel":[196],"design,":[197],"totally":[201],"parallelized":[202],"mainstream":[204],"training":[205,220,308,323],"framework.":[206],"Then,":[207],"find":[209],"size":[211],"that":[215,304],"recalled":[217],"by":[218],"each":[219],"sample":[221,233],"has":[222],"great":[224],"impact":[225],"on":[226,245,330],"final":[228],"accuracy,":[229],"so":[230],"introduce":[232],"grouping":[234],"optimization":[235],"well":[237],"approximate":[238],"full":[240],"training.":[242],"Experimental":[243],"evaluations":[244],"tasks":[247],"(Embedding":[248],"Learning":[249],"Classification)":[251],"ten":[253,338],"datasets":[254],"MegaFace,":[256],"ImageNet,":[257],"SKU":[258],"datasets)":[259],"demonstrate":[260],"our":[261,313,331],"proposed":[262],"method":[263,314],"maintains":[264],"same":[266],"precision":[267],"Full":[269],"for":[271],"different":[272],"loss":[273],"objectives,":[274],"including":[275],"cross":[276],"entropy":[277],"loss,":[278,283],"ArcFace,":[279],"CosFace":[280],"D-Softmax":[282],"1/10":[286],"sampled":[287],"which":[289],"outperforms":[290],"state-of-the-art":[292],"techniques.":[293],"Moreover,":[294],"implement":[296],"Soft-max":[298],"complete":[301],"pipeline":[303],"accelerate":[306],"than":[310],"4.3X.":[311],"Equipped":[312],"256":[317],"GPUs":[318],"cluster,":[319],"time":[321],"classifier":[325],"300":[327],"million":[328],"SKU-300M":[332],"dataset":[333],"reduced":[336],"days.":[339]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2022-01-25T00:00:00"}
