{"id":"https://openalex.org/W2971130703","doi":"https://doi.org/10.1109/tpami.2019.2937294","title":"Deep Differentiable Random Forests for Age Estimation","display_name":"Deep Differentiable Random Forests for Age Estimation","publication_year":2019,"publication_date":"2019-08-27","ids":{"openalex":"https://openalex.org/W2971130703","doi":"https://doi.org/10.1109/tpami.2019.2937294","mag":"2971130703","pmid":"https://pubmed.ncbi.nlm.nih.gov/31449007"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2019.2937294","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2019.2937294","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":null,"display_name":"Wei Shen","orcid":"https://orcid.org/0000-0002-1235-598X"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Shen","raw_affiliation_strings":["Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA"],"raw_orcid":"https://orcid.org/0000-0002-1235-598X","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yilu Guo","orcid":"https://orcid.org/0000-0002-5551-7053"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yilu Guo","raw_affiliation_strings":["School of Communication and Information Engineering, Shanghai University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-5551-7053","affiliations":[{"raw_affiliation_string":"School of Communication and Information Engineering, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yan Wang","raw_affiliation_strings":["Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kai Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Zhao","raw_affiliation_strings":["College of Computer and Control Engineering, Nankai University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Control Engineering, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bo Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I1325899441","display_name":"University Health Network","ror":"https://ror.org/042xt5161","country_code":"CA","type":"healthcare","lineage":["https://openalex.org/I1325899441"]},{"id":"https://openalex.org/I4210127509","display_name":"Vector Institute","ror":"https://ror.org/03kqdja62","country_code":"CA","type":"facility","lineage":["https://openalex.org/I4210127509"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Bo Wang","raw_affiliation_strings":["Vector Institute, Peter Munk Cardiac Center of University Health Network, Toronto, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vector Institute, Peter Munk Cardiac Center of University Health Network, Toronto, ON, Canada","institution_ids":["https://openalex.org/I4210127509","https://openalex.org/I1325899441"]}]},{"author_position":"last","author":{"id":null,"display_name":"Alan Yuille","orcid":null},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alan Yuille","raw_affiliation_strings":["Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.6798,"has_fulltext":false,"cited_by_count":79,"citation_normalized_percentile":{"value":0.9245375,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"43","issue":"2","first_page":"404","last_page":"419"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9616000056266785,"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":0.9616000056266785,"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/T11322","display_name":"Facial Rejuvenation and Surgery Techniques","score":0.005400000140070915,"subfield":{"id":"https://openalex.org/subfields/2708","display_name":"Dermatology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.004600000102072954,"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/deep-learning","display_name":"Deep learning","score":0.5909000039100647},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.5871999859809875},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5692999958992004},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5490000247955322},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5318999886512756},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47620001435279846},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4713999927043915},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4239000082015991}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6600000262260437},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5909000039100647},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.5871999859809875},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5692999958992004},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5490000247955322},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5318999886512756},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48510000109672546},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47620001435279846},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4713999927043915},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46140000224113464},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4239000082015991},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.37459999322891235},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3555000126361847},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3262999951839447},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3231000006198883},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3003000020980835},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2906999886035919}],"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":"D005145","descriptor_name":"Face","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005145","descriptor_name":"Face","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005145","descriptor_name":"Face","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007858","descriptor_name":"Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007858","descriptor_name":"Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007858","descriptor_name":"Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"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.2019.2937294","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2019.2937294","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:31449007","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31449007","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/G3759526236","display_name":"\u57fa\u4e8e\u81ea\u7136\u56fe\u50cf\u4e2d\u6df1\u5ea6\u5f62\u72b6\u7279\u5f81\u63d0\u53d6\u7684\u4e00\u822c\u7269\u4f53\u68c0\u6d4b\u7814\u7a76","funder_award_id":"61672336","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":60,"referenced_works":["https://openalex.org/W101414327","https://openalex.org/W137456267","https://openalex.org/W1516111018","https://openalex.org/W1905153633","https://openalex.org/W1972960842","https://openalex.org/W2007463795","https://openalex.org/W2009088607","https://openalex.org/W2009441118","https://openalex.org/W2036565334","https://openalex.org/W2039913439","https://openalex.org/W2044730627","https://openalex.org/W2066454034","https://openalex.org/W2067425370","https://openalex.org/W2067431615","https://openalex.org/W2075875861","https://openalex.org/W2103077782","https://openalex.org/W2104597750","https://openalex.org/W2106488920","https://openalex.org/W2113242816","https://openalex.org/W2115394472","https://openalex.org/W2118664399","https://openalex.org/W2120240539","https://openalex.org/W2133621813","https://openalex.org/W2146656095","https://openalex.org/W2147278565","https://openalex.org/W2151386286","https://openalex.org/W2151451863","https://openalex.org/W2155893237","https://openalex.org/W2161877964","https://openalex.org/W2164598857","https://openalex.org/W2166765763","https://openalex.org/W2170854557","https://openalex.org/W2172803778","https://openalex.org/W2220384803","https://openalex.org/W2274745179","https://openalex.org/W2330485005","https://openalex.org/W2436453945","https://openalex.org/W2440214111","https://openalex.org/W2510725918","https://openalex.org/W2553156677","https://openalex.org/W2579021424","https://openalex.org/W2612246789","https://openalex.org/W2747641531","https://openalex.org/W2748140016","https://openalex.org/W2777551880","https://openalex.org/W2798868324","https://openalex.org/W2911964244","https://openalex.org/W2949624713","https://openalex.org/W2962851632","https://openalex.org/W6610947227","https://openalex.org/W6627801759","https://openalex.org/W6631767438","https://openalex.org/W6637373629","https://openalex.org/W6640462745","https://openalex.org/W6674650171","https://openalex.org/W6676180231","https://openalex.org/W6684191040","https://openalex.org/W6696356757","https://openalex.org/W6732436211","https://openalex.org/W6734338310"],"related_works":["https://openalex.org/W4281616679","https://openalex.org/W4223564025","https://openalex.org/W2738221750","https://openalex.org/W4285586943","https://openalex.org/W4287776258","https://openalex.org/W4281780675","https://openalex.org/W2621864722","https://openalex.org/W564581980","https://openalex.org/W3118338202","https://openalex.org/W4206096925"],"abstract_inverted_index":{"Age":[0],"estimation":[1,198],"from":[2],"facial":[3,27,40],"images":[4],"is":[5,18,25,32],"typically":[6],"cast":[7],"as":[8],"a":[9,19],"label":[10],"distribution":[11],"learning":[12,102,119,161],"or":[13],"regression":[14],"problem,":[15],"since":[16],"aging":[17],"gradual":[20],"progress.":[21],"Its":[22],"main":[23],"challenge":[24],"the":[26,36,46,50,87,107,114,126,131,135,142,147,160,163],"feature":[28],"space":[29],"w.r.t.":[30],"ages":[31],"inhomogeneous,":[33],"due":[34],"to":[35,86,169],"large":[37],"variation":[38],"in":[39],"appearance":[41],"across":[42],"different":[43],"persons":[44],"of":[45,53,81,90,162,178,182],"same":[47],"age":[48,78,111,197],"and":[49,72,95,110,129,134,145,165,174,190],"non-stationary":[51],"property":[52],"aging.":[54],"In":[55],"this":[56],"paper,":[57],"we":[58],"propose":[59],"two":[60],"Deep":[61,66,73],"Differentiable":[62],"Random":[63],"Forests":[64],"methods,":[65],"Label":[67],"Distribution":[68],"Learning":[69],"Forest":[70,75],"(DLDLF)":[71],"Regression":[74],"(DRF),":[76],"for":[77],"estimation.":[79],"Both":[80],"them":[82],"connect":[83],"split":[84,108,132,143,164],"nodes":[85,109,128,133,144,149],"top":[88],"layer":[89],"convolutional":[91],"neural":[92],"networks":[93],"(CNNs)":[94],"deal":[96],"with":[97],"inhomogeneous":[98],"data":[99,104],"by":[100,138,150],"jointly":[101],"input-dependent":[103],"partitions":[105],"at":[106,113],"distributions":[112],"leaf":[115,127,148,166],"nodes.":[116],"This":[117],"joint":[118],"follows":[120],"an":[121],"alternating":[122],"strategy:":[123],"(1)":[124],"Fixing":[125,141],"optimizing":[130,146],"CNN":[136],"parameters":[137,180],"Back-propagation;":[139],"(2)":[140],"Variational":[151],"Bounding.":[152],"Two":[153],"Deterministic":[154],"Annealing":[155],"processes":[156],"are":[157],"introduced":[158],"into":[159],"nodes,":[167],"respectively,":[168],"avoid":[170],"poor":[171],"local":[172],"optima":[173],"obtain":[175],"better":[176],"estimates":[177],"tree":[179],"free":[181],"initial":[183],"values.":[184],"Experimental":[185],"results":[186],"show":[187],"that":[188],"DLDLF":[189],"DRF":[191],"achieve":[192],"state-of-the-art":[193],"performance":[194],"on":[195],"three":[196],"datasets.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":5}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2019-09-05T00:00:00"}