{"id":"https://openalex.org/W7140306952","doi":"https://doi.org/10.48550/arxiv.2603.22965","title":"Few-Shot Generative Model Adaption via Identity Injection and Preservation","display_name":"Few-Shot Generative Model Adaption via Identity Injection and Preservation","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140306952","doi":"https://doi.org/10.48550/arxiv.2603.22965"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.22965","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22965","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.22965","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130558865","display_name":"Yeqi He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Yeqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130565496","display_name":"Liang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130562346","display_name":"Jiehua Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jiehua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048712886","display_name":"Yaoqi Sun","orcid":"https://orcid.org/0000-0001-8874-241X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Yaoqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129744842","display_name":"Xichun Sheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheng, Xichun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086944905","display_name":"Zhidong Zhao","orcid":"https://orcid.org/0009-0008-7945-8466"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Zhidong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130626475","display_name":"Chenggang Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Chenggang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9416000247001648,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9416000247001648,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.02319999970495701,"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/T11448","display_name":"Face recognition and analysis","score":0.006300000008195639,"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/identity","display_name":"Identity (music)","score":0.8039000034332275},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5831000208854675},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.47999998927116394},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4578999876976013},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.45350000262260437},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.3944999873638153}],"concepts":[{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.8039000034332275},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6531999707221985},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5831000208854675},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.47999998927116394},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4578999876976013},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.45350000262260437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43549999594688416},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.27309998869895935},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2556999921798706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.22965","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22965","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.22965","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22965","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Training":[0],"generative":[1,21,37],"models":[2],"with":[3,27],"limited":[4],"data":[5],"presents":[6],"severe":[7],"challenges":[8],"of":[9,57,115],"mode":[10],"collapse.":[11],"A":[12],"common":[13],"approach":[14],"is":[15],"to":[16,81,135],"adapt":[17],"a":[18,24,128,132],"large":[19],"pretrained":[20],"model":[22,38],"upon":[23],"target":[25,62,103],"domain":[26,48,98,139],"very":[28],"few":[29],"samples":[30],"(fewer":[31],"than":[32],"10),":[33],"known":[34],"as":[35],"few-shot":[36],"adaptation.":[39],"However,":[40],"existing":[41],"methods":[42,171],"often":[43],"suffer":[44],"from":[45,150],"forgetting":[46],"source":[47,84,97,117,138],"identity":[49,76,85,92,99,113,123,140,144,151,155],"knowledge":[50,100,114],"during":[51],"adaptation,":[52],"which":[53,74,126],"degrades":[54],"the":[55,61,83,102,108,116],"quality":[56],"generated":[58,109],"images":[59,110],"in":[60],"domain.":[63,118],"To":[64],"address":[65],"this,":[66],"we":[67,88,120],"propose":[68],"Identity":[69],"Injection":[70],"and":[71,78,131,159,176],"Preservation":[72],"(I$^2$P),":[73],"leverages":[75],"injection":[77,93],"consistency":[79,145],"alignment":[80],"preserve":[82],"knowledge.":[86,156],"Specifically,":[87],"first":[89],"introduce":[90],"an":[91,122],"module":[94],"that":[95,163],"integrates":[96],"into":[101],"domain's":[104],"latent":[105],"space,":[106],"ensuring":[107],"retain":[111],"key":[112],"Second,":[119],"design":[121],"substitution":[124],"module,":[125],"includes":[127],"style-content":[129],"decoupler":[130],"reconstruction":[133],"modulator,":[134],"further":[136],"enhance":[137],"preservation.":[141],"We":[142],"enforce":[143],"constraints":[146],"by":[147],"aligning":[148],"features":[149],"substitution,":[152],"thereby":[153],"preserving":[154],"Both":[157],"quantitative":[158],"qualitative":[160],"experiments":[161],"show":[162],"our":[164],"method":[165],"achieves":[166],"substantial":[167],"improvements":[168],"over":[169],"state-of-the-art":[170],"on":[172],"multiple":[173],"public":[174],"datasets":[175],"5":[177],"metrics.":[178]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-26T00:00:00"}
