{"id":"https://openalex.org/W2798989084","doi":"https://doi.org/10.1145/3209978.3210155","title":"Deep Domain Adaptation Based on Multi-layer Joint Kernelized Distance","display_name":"Deep Domain Adaptation Based on Multi-layer Joint Kernelized Distance","publication_year":2018,"publication_date":"2018-06-27","ids":{"openalex":"https://openalex.org/W2798989084","doi":"https://doi.org/10.1145/3209978.3210155","mag":"2798989084"},"language":"en","primary_location":{"id":"doi:10.1145/3209978.3210155","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3209978.3210155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 41st International ACM SIGIR Conference on Research &amp; Development in Information Retrieval","raw_type":"proceedings-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/A5062329742","display_name":"Sitong Mao","orcid":"https://orcid.org/0000-0003-2490-2896"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Sitong Mao","raw_affiliation_strings":["The Hong Kong Polytechnic University, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114375282","display_name":"Xiao Shen","orcid":"https://orcid.org/0000-0003-0937-049X"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiao Shen","raw_affiliation_strings":["The Hong Kong Polytechnic University, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043016512","display_name":"Fu-Lai Chung","orcid":"https://orcid.org/0000-0001-5294-8168"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Fu-lai Chung","raw_affiliation_strings":["The Hong Kong Polytechnic University, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5062329742"],"corresponding_institution_ids":["https://openalex.org/I14243506"],"apc_list":null,"apc_paid":null,"fwci":0.9773,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.81389085,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1049","last_page":"1052"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998999834060669,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9861999750137329,"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/T12676","display_name":"Machine Learning and ELM","score":0.9125999808311462,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.8164107799530029},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7608978152275085},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.6467596292495728},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6381123661994934},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.6306920051574707},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5951380729675293},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5948210954666138},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4833902418613434},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.45825234055519104},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4309138059616089},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41591253876686096},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32665666937828064},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16154628992080688}],"concepts":[{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.8164107799530029},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7608978152275085},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.6467596292495728},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6381123661994934},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.6306920051574707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5951380729675293},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5948210954666138},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4833902418613434},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.45825234055519104},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4309138059616089},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41591253876686096},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32665666937828064},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16154628992080688},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3209978.3210155","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3209978.3210155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 41st International ACM SIGIR Conference on Research &amp; Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1565327149","https://openalex.org/W1722318740","https://openalex.org/W2115403315","https://openalex.org/W2149466042","https://openalex.org/W2155893237","https://openalex.org/W2158815628","https://openalex.org/W2407712691","https://openalex.org/W2463241543","https://openalex.org/W2604272474","https://openalex.org/W2618530766","https://openalex.org/W2740063619","https://openalex.org/W2740997336","https://openalex.org/W2951670162","https://openalex.org/W2964278684"],"related_works":["https://openalex.org/W3035557009","https://openalex.org/W3204418343","https://openalex.org/W2341113105","https://openalex.org/W2955172689","https://openalex.org/W3132602785","https://openalex.org/W3046182208","https://openalex.org/W2343346879","https://openalex.org/W2186589590","https://openalex.org/W2531741693","https://openalex.org/W4297818280"],"abstract_inverted_index":{"Domain":[0],"adaptation":[1],"refers":[2],"to":[3,56,96,98,119,147],"the":[4,12,18,23,51,58,65,72,78,82,99,108,120,123,130,138],"learning":[5],"scenario":[6],"where":[7],"a":[8,143,148],"model":[9,126],"learned":[10],"from":[11,50],"source":[13,79],"data":[14,20,69,80,90,132],"is":[15],"applied":[16],"on":[17,129],"target":[19,68,89,131],"which":[21],"have":[22],"same":[24],"categories":[25],"but":[26],"different":[27],"distributions.":[28],"In":[29,42],"information":[30],"retrieval,":[31],"there":[32],"exist":[33],"application":[34],"scenarios":[35],"like":[36],"cross":[37],"domain":[38],"recommendation":[39],"characterized":[40],"similarly.":[41],"this":[43],"paper,":[44],"by":[45,106],"utilizing":[46],"deep":[47,52,125],"features":[48],"extracted":[49],"networks,":[53],"we":[54],"proposed":[55,139],"compute":[57],"multi-layer":[59],"joint":[60],"kernelized":[61],"mean":[62],"distance":[63,110],"between":[64],"k":[66],"th":[67,74,84,101],"predicted":[70],"as":[71],"i":[73,100],"category":[75,85,102],"and":[76],"all":[77],"of":[81,150],"j":[83],"$d_ij":[86],"^k$.":[87,114],"Then,":[88],"$T_m$":[91,118],"that":[92,137],"are":[93],"most":[94],"likely":[95],"belong":[97],"can":[103,127,141],"be":[104],"found":[105],"calculating":[107],"relative":[109],"$d_ii":[111],"^k/\\sum_j":[112],"d_ij":[113],"By":[115],"iteratively":[116],"adding":[117],"training":[121],"data,":[122],"finetuned":[124],"adapt":[128],"progressively.":[133],"Our":[134],"results":[135],"demonstrate":[136],"method":[140],"achieve":[142],"better":[144],"performance":[145],"compared":[146],"number":[149],"state-of-the-art":[151],"methods.":[152]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
