{"id":"https://openalex.org/W4303981349","doi":"https://doi.org/10.3390/s22197579","title":"Deep Metric Learning Using Negative Sampling Probability Annealing","display_name":"Deep Metric Learning Using Negative Sampling Probability Annealing","publication_year":2022,"publication_date":"2022-10-06","ids":{"openalex":"https://openalex.org/W4303981349","doi":"https://doi.org/10.3390/s22197579","pmid":"https://pubmed.ncbi.nlm.nih.gov/36236678"},"language":"en","primary_location":{"id":"doi:10.3390/s22197579","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22197579","pdf_url":"https://www.mdpi.com/1424-8220/22/19/7579/pdf?version=1665316853","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/19/7579/pdf?version=1665316853","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084426852","display_name":"G\u00e1bor Kert\u00e9sz","orcid":"https://orcid.org/0000-0002-8845-8301"},"institutions":[{"id":"https://openalex.org/I103356709","display_name":"Obuda University","ror":"https://ror.org/00ax71d21","country_code":"HU","type":"education","lineage":["https://openalex.org/I103356709"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"G\u00e1bor Kert\u00e9sz","raw_affiliation_strings":["John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary"],"raw_orcid":"https://orcid.org/0000-0002-8845-8301","affiliations":[{"raw_affiliation_string":"John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary","institution_ids":["https://openalex.org/I103356709"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5084426852"],"corresponding_institution_ids":["https://openalex.org/I103356709"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2673},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2673},"fwci":0.1619,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.41482494,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"22","issue":"19","first_page":"7579","last_page":"7579"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9997000098228455,"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/T11448","display_name":"Face recognition and analysis","score":0.9997000098228455,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9987000226974487,"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":0.9979000091552734,"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/discriminative-model","display_name":"Discriminative model","score":0.7441080808639526},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6859258413314819},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5990355610847473},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5734397768974304},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5593962669372559},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.5162129998207092},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.514563262462616},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.513633668422699},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.49636226892471313},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4865228533744812},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.463830828666687},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.45456838607788086},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32162874937057495},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14139771461486816}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7441080808639526},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6859258413314819},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5990355610847473},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5734397768974304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5593962669372559},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.5162129998207092},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.514563262462616},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.513633668422699},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.49636226892471313},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4865228533744812},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.463830828666687},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.45456838607788086},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32162874937057495},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14139771461486816},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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":"D011336","descriptor_name":"Probability","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011336","descriptor_name":"Probability","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011336","descriptor_name":"Probability","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":5,"locations":[{"id":"doi:10.3390/s22197579","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22197579","pdf_url":"https://www.mdpi.com/1424-8220/22/19/7579/pdf?version=1665316853","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:36236678","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36236678","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":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:9572431","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9572431","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:1cd94dae4e18470da3aee6274736591e","is_oa":true,"landing_page_url":"https://doaj.org/article/1cd94dae4e18470da3aee6274736591e","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 22, Iss 19, p 7579 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/19/7579/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22197579","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 22; Issue 19; Pages: 7579","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s22197579","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22197579","pdf_url":"https://www.mdpi.com/1424-8220/22/19/7579/pdf?version=1665316853","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4303981349.pdf","grobid_xml":"https://content.openalex.org/works/W4303981349.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W1869500417","https://openalex.org/W1922045146","https://openalex.org/W2096733369","https://openalex.org/W2114168642","https://openalex.org/W2126017757","https://openalex.org/W2127589108","https://openalex.org/W2145287260","https://openalex.org/W2408241409","https://openalex.org/W2470394683","https://openalex.org/W2512434173","https://openalex.org/W2513140567","https://openalex.org/W2606611007","https://openalex.org/W2792129423","https://openalex.org/W2797558164","https://openalex.org/W2919115771","https://openalex.org/W2921310091","https://openalex.org/W2962898354","https://openalex.org/W2963350250","https://openalex.org/W2963466847","https://openalex.org/W2963534981","https://openalex.org/W2963775347","https://openalex.org/W2964305564","https://openalex.org/W2966961956","https://openalex.org/W2999322490","https://openalex.org/W2999711171","https://openalex.org/W3011302436","https://openalex.org/W3014654539","https://openalex.org/W3023512465","https://openalex.org/W3024272706","https://openalex.org/W3030118121","https://openalex.org/W3034693849","https://openalex.org/W3086505308","https://openalex.org/W3090114880","https://openalex.org/W3109684201","https://openalex.org/W3109894131","https://openalex.org/W3114146310","https://openalex.org/W3123291924","https://openalex.org/W3135935488","https://openalex.org/W3185200950","https://openalex.org/W3200115061","https://openalex.org/W4221047184","https://openalex.org/W4224941545","https://openalex.org/W4226344269","https://openalex.org/W4313043855","https://openalex.org/W6713134421","https://openalex.org/W6725923168","https://openalex.org/W6783961830"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"Multiple":[0],"studies":[1],"have":[2],"concluded":[3],"that":[4,61],"the":[5,19,22,37,41,43,104,107,142,159],"selection":[6,20,35,105],"of":[7,21,36,52,106,144,162],"input":[8],"samples":[9],"is":[10,28,39,56],"key":[11],"for":[12,73],"deep":[13],"metric":[14],"learning.":[15],"For":[16],"triplet":[17,32,66],"networks,":[18],"anchor,":[23],"positive,":[24],"and":[25],"negative":[26,60,75,97,110,123,134],"pairs":[27],"referred":[29,131],"to":[30,48,57,84,94,132,140],"as":[31,133],"mining.":[33],"The":[34,54],"negatives":[38],"considered":[40],"be":[42],"most":[44],"complicated":[45],"task,":[46],"due":[47],"a":[49,59,64,121],"large":[50],"number":[51],"possibilities.":[53],"goal":[55],"select":[58],"results":[62,114],"in":[63,82,101,115],"positive":[65],"loss;":[67],"however,":[68,100],"there":[69],"are":[70,80,148,165],"multiple":[71],"approaches":[72],"this-semi-hard":[74],"mining":[76,79,91,98],"or":[77],"hardest":[78,109],"well-known":[81],"addition":[83],"random":[85],"selection.":[86],"Since":[87],"its":[88],"introduction,":[89],"semi-hard":[90],"was":[92],"proven":[93],"outperform":[95],"other":[96],"techniques;":[99],"recent":[102],"years,":[103],"so-called":[108],"has":[111],"shown":[112],"promising":[113],"different":[116],"experiments.":[117],"This":[118],"paper":[119],"introduces":[120],"novel":[122],"sampling":[124,135],"solution":[125],"based":[126],"on":[127,150,167],"dynamic":[128],"policy":[129],"switching,":[130],"probability":[136],"annealing,":[137],"which":[138],"aims":[139],"exploit":[141],"positives":[143],"all":[145],"approaches.":[146],"Results":[147],"validated":[149],"an":[151],"experimental":[152],"synthetic":[153],"dataset":[154],"using":[155],"cluster-analysis":[156],"methods;":[157],"finally,":[158],"discriminative":[160],"abilities":[161],"trained":[163],"models":[164],"measured":[166],"real-life":[168],"data.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}