{"id":"https://openalex.org/W4415707713","doi":"https://doi.org/10.1109/icme59968.2025.11210063","title":"Assessing the Generalizability of Deep Models without Out-of-Distribution Data","display_name":"Assessing the Generalizability of Deep Models without Out-of-Distribution Data","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415707713","doi":"https://doi.org/10.1109/icme59968.2025.11210063"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11210063","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210063","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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/A5048329080","display_name":"Guoqing Zhu","orcid":"https://orcid.org/0000-0002-5600-9034"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoqing Zhu","raw_affiliation_strings":["Fuzhou University,College of Computer and Data Science,Fuzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,College of Computer and Data Science,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120192657","display_name":"Xiaojie Gan","orcid":null},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojie Gan","raw_affiliation_strings":["Fuzhou University,College of Computer and Data Science,Fuzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,College of Computer and Data Science,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lingye Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingye Zhao","raw_affiliation_strings":["Fuzhou University,College of Computer and Data Science,Fuzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,College of Computer and Data Science,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010577514","display_name":"Luojun Lin","orcid":"https://orcid.org/0000-0002-1141-2487"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luojun Lin","raw_affiliation_strings":["Fuzhou University,College of Computer and Data Science,Fuzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,College of Computer and Data Science,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I80947539"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14566444,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.8995000123977661,"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":0.8995000123977661,"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/T10028","display_name":"Topic Modeling","score":0.03840000182390213,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.00570000009611249,"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/generalization","display_name":"Generalization","score":0.7746000289916992},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.7239000201225281},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5706999897956848},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5264999866485596},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.42719998955726624},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41780000925064087},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3946000039577484}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7746000289916992},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.7239000201225281},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.650600016117096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5838000178337097},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5706999897956848},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5264999866485596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4975999891757965},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42719998955726624},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41780000925064087},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3946000039577484},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.3799000084400177},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3752000033855438},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37290000915527344},{"id":"https://openalex.org/C5465570","wikidata":"https://www.wikidata.org/wiki/Q5326898","display_name":"Early stopping","level":3,"score":0.3452000021934509},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.3368000090122223},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.26969999074935913}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11210063","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210063","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2167366427","https://openalex.org/W2194775991","https://openalex.org/W2627183927","https://openalex.org/W2763549966","https://openalex.org/W2906405631","https://openalex.org/W2963043696","https://openalex.org/W2981540341","https://openalex.org/W2998712190","https://openalex.org/W3011304563","https://openalex.org/W3034373371","https://openalex.org/W3133542152","https://openalex.org/W3175481246","https://openalex.org/W3179233662","https://openalex.org/W3209861741","https://openalex.org/W4382318733","https://openalex.org/W4399929809"],"related_works":[],"abstract_inverted_index":{"Existing":[0],"domain":[1,9,17,26,38,60,135],"generalization":[2,21,51,72,90,161],"(DG)":[3],"methods":[4,45],"typically":[5],"rely":[6],"on":[7,64,127,150],"source":[8,37,134],"data":[10,18,27],"for":[11,74],"model":[12,84,107,117],"training":[13,77],"and":[14,88,118,147],"unknown":[15,58],"target":[16,25,59],"to":[19,48],"evaluate":[20],"ability.":[22],"However,":[23],"collecting":[24,143],"is":[28,46],"challenging":[29],"because":[30],"it":[31],"must":[32],"be":[33],"out-of-distribution":[34],"from":[35],"the":[36,50,94,115,133,160],"data.":[39,66,136],"Thus,":[40],"developing":[41],"Target-Free":[42],"Evaluation":[43],"(TFEval)":[44],"essential":[47],"assess":[49],"performance":[52,73,162],"of":[53,132,163],"deep":[54,145,164],"models":[55,124],"without":[56],"requiring":[57],"data,":[61],"relying":[62],"only":[63],"trained":[65,116,126],"In":[67],"this":[68],"paper,":[69],"we":[70,105],"estimate":[71],"TFEval":[75,140],"by":[76,93,109,142],"a":[78,128],"regression":[79],"predictor.":[80],"The":[81,122],"predictor":[82],"takes":[83],"representations":[85,108],"as":[86],"input":[87],"predicts":[89,159],"performance.":[91],"Inspired":[92],"observation":[95],"that":[96,154],"Batch":[97],"Normalization":[98],"(BN)":[99],"parameters":[100],"strongly":[101],"reflect":[102],"domain/distribution":[103],"information,":[104],"extract":[106],"calculating":[110],"BN":[111],"parameter":[112],"differences":[113],"between":[114],"its":[119],"proxy":[120,123],"models.":[121,165],"are":[125],"significantly":[129],"smaller":[130],"subset":[131],"We":[137],"construct":[138],"multiple":[139],"datasets":[141,152],"numerous":[144],"models,":[146],"experimental":[148],"results":[149],"these":[151],"demonstrate":[153],"our":[155],"evaluation":[156],"method":[157],"effectively":[158]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-30T00:00:00"}
