{"id":"https://openalex.org/W4416286227","doi":"https://doi.org/10.1109/lgrs.2025.3633718","title":"Geospatial Domain Adaptation With Truncated Parameter-Efficient Fine-Tuning","display_name":"Geospatial Domain Adaptation With Truncated Parameter-Efficient Fine-Tuning","publication_year":2025,"publication_date":"2025-11-17","ids":{"openalex":"https://openalex.org/W4416286227","doi":"https://doi.org/10.1109/lgrs.2025.3633718"},"language":null,"primary_location":{"id":"doi:10.1109/lgrs.2025.3633718","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2025.3633718","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Geoscience and Remote Sensing Letters","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/A5079956325","display_name":"Kwonyoung Kim","orcid":"https://orcid.org/0009-0008-3727-5872"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Kwonyoung Kim","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082917289","display_name":"Jungin Park","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jungin Park","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073320959","display_name":"Kwanghoon Sohn","orcid":"https://orcid.org/0000-0002-3715-0331"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kwanghoon Sohn","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5079956325"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18396099,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"23","issue":null,"first_page":"1","last_page":"5"},"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":0.8781999945640564,"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":0.8781999945640564,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.060600001364946365,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.02630000002682209,"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/set","display_name":"Set (abstract data type)","score":0.5665000081062317},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.5550000071525574},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5351999998092651},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5232999920845032},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.46700000762939453},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.4334000051021576},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.38449999690055847}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8129000067710876},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5665000081062317},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.5550000071525574},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5351999998092651},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5232999920845032},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.46700000762939453},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.4334000051021576},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4221000075340271},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4023999869823456},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3880000114440918},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.38449999690055847},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.30730000138282776},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3025999963283539},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.2718000113964081},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.26829999685287476},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.26649999618530273},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2025.3633718","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2025.3633718","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1200101808","display_name":null,"funder_award_id":"RS-2025-00515741","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1980038761","https://openalex.org/W2108598243","https://openalex.org/W2592962403","https://openalex.org/W2962777203","https://openalex.org/W2963785576","https://openalex.org/W2964194231","https://openalex.org/W3200126366","https://openalex.org/W3213288046","https://openalex.org/W4313156423","https://openalex.org/W4319594870","https://openalex.org/W4387828764","https://openalex.org/W4390781335","https://openalex.org/W4390874379","https://openalex.org/W4391306135","https://openalex.org/W4393305465","https://openalex.org/W4396782973","https://openalex.org/W4402727589","https://openalex.org/W4404708688","https://openalex.org/W4413144534"],"related_works":[],"abstract_inverted_index":{"Parameter-efficient":[0],"fine-tuning":[1],"(PEFT)":[2],"adapts":[3],"large":[4],"pre-trained":[5,28,51],"foundation":[6],"models":[7],"to":[8,47,73,111,119],"downstream":[9],"tasks,":[10],"such":[11],"as":[12],"remote":[13,126,133],"sensing":[14,127,134],"scene":[15,135],"classification,":[16],"by":[17],"learning":[18],"a":[19,61,91,100,107],"small":[20],"set":[21,92],"of":[22,93,141],"additional":[23],"parameters":[24,29],"while":[25,153],"keeping":[26],"the":[27,116,139,142],"frozen.":[30],"While":[31],"PEFT":[32,63,162],"offers":[33],"substantial":[34],"training":[35],"efficiency":[36,71],"over":[37],"full":[38,165],"fine-tuning,":[39],"it":[40],"still":[41],"incurs":[42],"high":[43,87],"inference":[44],"costs":[45],"due":[46],"reliance":[48],"on":[49,131],"both":[50],"and":[52,82,96,149,164],"task-specific":[53],"parameters.":[54],"To":[55],"address":[56],"this":[57],"limitation,":[58],"we":[59,89,105],"propose":[60],"novel":[62],"approach":[64],"with":[65,99],"model":[66],"truncation,":[67],"termed":[68],"TruncPEFT,":[69],"enabling":[70],"gains":[72],"persist":[74],"during":[75],"inference.":[76],"Observing":[77],"that":[78],"predictions":[79],"from":[80],"final":[81,94],"intermediate":[83],"layers":[84,95],"often":[85],"exhibit":[86],"agreement,":[88],"truncate":[90],"replace":[97],"them":[98],"lightweight":[101],"attention":[102],"module.":[103],"Additionally,":[104],"introduce":[106],"token":[108],"dropping":[109],"strategy":[110],"mitigate":[112],"interclass":[113],"interference,":[114],"reducing":[115],"model\u2019s":[117],"sensitivity":[118],"visual":[120],"similarities":[121],"between":[122],"different":[123],"classes":[124],"in":[125],"data.":[128],"Extensive":[129],"experiments":[130],"seven":[132],"classification":[136],"datasets":[137],"demonstrate":[138],"effectiveness":[140],"proposed":[143],"method,":[144],"significantly":[145],"improving":[146],"training,":[147],"inference,":[148],"GPU":[150],"memory":[151],"efficiencies":[152],"achieving":[154],"comparable":[155],"or":[156],"even":[157],"better":[158],"performance":[159],"than":[160],"prior":[161],"methods":[163],"fine-tuning.":[166]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-17T00:00:00"}