{"id":"https://openalex.org/W2889705046","doi":"https://doi.org/10.1109/wacv.2019.00045","title":"Instance-Based Deep Transfer Learning","display_name":"Instance-Based Deep Transfer Learning","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2889705046","doi":"https://doi.org/10.1109/wacv.2019.00045","mag":"2889705046"},"language":"en","primary_location":{"id":"doi:10.1109/wacv.2019.00045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2019.00045","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","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/A5100656834","display_name":"Tianyang Wang","orcid":"https://orcid.org/0000-0003-3184-0566"},"institutions":[{"id":"https://openalex.org/I184692499","display_name":"Austin Peay State University","ror":"https://ror.org/05tx3bv88","country_code":"US","type":"education","lineage":["https://openalex.org/I184692499"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tianyang Wang","raw_affiliation_strings":["Austin Peay State University"],"affiliations":[{"raw_affiliation_string":"Austin Peay State University","institution_ids":["https://openalex.org/I184692499"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080409305","display_name":"Jun Huan","orcid":"https://orcid.org/0000-0002-7020-1604"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Huan","raw_affiliation_strings":["Baidu Research"],"affiliations":[{"raw_affiliation_string":"Baidu Research","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006863048","display_name":"Michelle Zhu","orcid":"https://orcid.org/0000-0003-1715-4041"},"institutions":[{"id":"https://openalex.org/I166088655","display_name":"Montclair State University","ror":"https://ror.org/01nxc2t48","country_code":"US","type":"education","lineage":["https://openalex.org/I166088655"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michelle Zhu","raw_affiliation_strings":["Montclair State University"],"affiliations":[{"raw_affiliation_string":"Montclair State University","institution_ids":["https://openalex.org/I166088655"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100656834"],"corresponding_institution_ids":["https://openalex.org/I184692499"],"apc_list":null,"apc_paid":null,"fwci":2.7458,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.92359206,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"367","last_page":"375"},"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":1.0,"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":1.0,"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/T12676","display_name":"Machine Learning and ELM","score":0.9936000108718872,"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":0.9922000169754028,"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/transfer-of-learning","display_name":"Transfer of learning","score":0.8877769112586975},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8281811475753784},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8252102732658386},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.8205727934837341},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7222921848297119},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6363720893859863},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6208878755569458},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5374285578727722},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5046771764755249},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4663117825984955}],"concepts":[{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.8877769112586975},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8281811475753784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8252102732658386},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8205727934837341},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7222921848297119},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6363720893859863},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6208878755569458},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5374285578727722},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5046771764755249},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4663117825984955},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv.2019.00045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2019.00045","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320312440","display_name":"Montclair State University","ror":"https://ror.org/01nxc2t48"},{"id":"https://openalex.org/F4320314046","display_name":"Austin Peay State University","ror":"https://ror.org/05tx3bv88"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W1665214252","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W2108598243","https://openalex.org/W2156749117","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2335728318","https://openalex.org/W2561238782","https://openalex.org/W2591924527","https://openalex.org/W2593768305","https://openalex.org/W2597603852","https://openalex.org/W2734358244","https://openalex.org/W2740053211","https://openalex.org/W2962835968","https://openalex.org/W2963070594","https://openalex.org/W2963446712","https://openalex.org/W2964121744","https://openalex.org/W2964278684","https://openalex.org/W3118608800","https://openalex.org/W4299518610","https://openalex.org/W6631943919","https://openalex.org/W6637242042","https://openalex.org/W6637373629","https://openalex.org/W6682132143","https://openalex.org/W6682764186","https://openalex.org/W6703116779","https://openalex.org/W6713955831","https://openalex.org/W6730179637","https://openalex.org/W6735632633","https://openalex.org/W6741852109","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W1529840045","https://openalex.org/W4244036394","https://openalex.org/W1842879116","https://openalex.org/W2135107501","https://openalex.org/W2124490386","https://openalex.org/W1822895636","https://openalex.org/W2760891738","https://openalex.org/W3193920202","https://openalex.org/W4318813552","https://openalex.org/W2576964996"],"abstract_inverted_index":{"Deep":[0],"transfer":[1,35,55,75,175],"learning":[2,36,56,76,176,180,201],"recently":[3],"has":[4,49],"acquired":[5],"significant":[6],"research":[7,47],"interest.":[8],"It":[9],"makes":[10],"use":[11],"of":[12,62,99,112,126,192,199],"pre-trained":[13,85,128,135,158],"models":[14,25,181,202],"that":[15,121],"are":[16],"learned":[17],"from":[18,87],"a":[19,30,78,84,88,103,148],"source":[20,89],"domain,":[21,145],"and":[22,91,160],"utilizes":[23],"these":[24],"for":[26,203],"the":[27,39,60,97,109,113,118,124,127,134,138,143,157,164,169,190,197],"tasks":[28],"in":[29,77,102,142,168],"target":[31,79,104,114,144,170],"domain.":[32,80,105,171],"Model-based":[33],"deep":[34,54,74,179,200],"is":[37,152],"probably":[38],"most":[40],"frequently":[41],"used":[42],"method.":[43],"However,":[44],"very":[45],"little":[46],"work":[48],"been":[50],"devoted":[51],"to":[52,72,95,182],"enhancing":[53],"by":[57,116],"focusing":[58],"on":[59,156,195],"influence":[61,98],"data.":[63],"In":[64],"this":[65,93,173],"paper,":[66],"we":[67,82,107],"propose":[68],"an":[69],"instance-based":[70],"approach":[71,194],"improve":[73],"Specifically,":[81],"choose":[83],"model":[86,94,136,150],"domain":[90,115],"apply":[92],"estimate":[96],"training":[100,110,119,140,166],"samples":[101,120],"Then":[106],"optimize":[108],"data":[111,141,167],"removing":[117],"will":[122],"lower":[123],"performance":[125],"model.":[129],"We":[130],"later":[131],"either":[132],"fine-tune":[133,161],"with":[137,163],"optimized":[139,165],"or":[146],"build":[147],"new":[149],"which":[151],"initialized":[153],"partially":[154],"based":[155],"model,":[159],"it":[162],"Using":[172],"approach,":[174],"can":[177],"help":[178],"capture":[183],"more":[184],"useful":[185],"features.":[186],"Extensive":[187],"experiments":[188],"demonstrate":[189],"effectiveness":[191],"our":[193],"boosting":[196],"quality":[198],"some":[204],"common":[205],"computer":[206],"vision":[207],"tasks,":[208],"such":[209],"as":[210],"image":[211],"classification.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":3}],"updated_date":"2026-04-20T07:46:08.049788","created_date":"2025-10-10T00:00:00"}
