{"id":"https://openalex.org/W6948410840","doi":"https://doi.org/10.48550/arxiv.2506.24093","title":"Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies","display_name":"Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W6948410840","doi":"https://doi.org/10.48550/arxiv.2506.24093"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2506.24093","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.24093","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"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.2506.24093","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Wachter, Paul","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wachter, Paul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Niehaus, Lukas","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niehaus, Lukas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Sch\u00f6ning, Julius","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sch\u00f6ning, Julius","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":true,"primary_topic":{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.05889999866485596,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.05889999866485596,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11926","display_name":"Subterranean biodiversity and taxonomy","score":0.05530000105500221,"subfield":{"id":"https://openalex.org/subfields/1911","display_name":"Paleontology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11937","display_name":"Research Data Management Practices","score":0.05339999869465828,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/robustness","display_name":"Robustness (evolution)","score":0.7541000247001648},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.7063000202178955},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.6934000253677368},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5727999806404114},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5256999731063843},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4562000036239624},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.44279998540878296},{"id":"https://openalex.org/keywords/real-world-data","display_name":"Real world data","score":0.361299991607666}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7541000247001648},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.7063000202178955},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.6934000253677368},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6610999703407288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6583999991416931},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6047000288963318},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5727999806404114},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5256999731063843},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4562000036239624},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.361299991607666},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34529998898506165},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C157170001","wikidata":"https://www.wikidata.org/wiki/Q4781507","display_name":"Applications of artificial intelligence","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C13926793","wikidata":"https://www.wikidata.org/wiki/Q3507155","display_name":"Group method of data handling","level":2,"score":0.25189998745918274},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2506.24093","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.24093","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2506.24093","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.24093","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"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":{"Synthetic":[0],"data":[1,10,24,130,154],"has":[2],"emerged":[3],"as":[4,63],"a":[5,27,80],"cost-effective":[6],"alternative":[7],"to":[8,33,44,52,75,128,131,163],"real":[9,23,60,129,138],"for":[11],"training":[12,65,157],"artificial":[13],"neural":[14],"networks":[15],"(ANN).":[16],"However,":[17],"the":[18,39,77,133,150,156],"disparity":[19],"between":[20],"synthetic":[21,58,127,136,153],"and":[22,36,59,86,91,112,137,166],"results":[25],"in":[26,155],"domain":[28,78],"gap.":[29],"That":[30],"gap":[31],"leads":[32],"poor":[34],"performance":[35],"generalization":[37],"of":[38,83,126,135,142,152,159],"trained":[40],"ANN":[41],"when":[42],"applied":[43],"real-world":[45],"scenarios.":[46],"Several":[47],"strategies":[48,71,107],"have":[49,72],"been":[50,73],"developed":[51],"bridge":[53],"this":[54,97,143],"gap,":[55,79],"which":[56],"combine":[57],"data,":[61],"known":[62],"mixed":[64],"using":[66],"hybrid":[67,115],"datasets.":[68,116],"While":[69],"these":[70,118],"shown":[74],"mitigate":[76],"systematic":[81],"evaluation":[82],"their":[84],"generalizability":[85],"robustness":[87,165],"across":[88],"various":[89],"tasks":[90],"architectures":[92,111],"remains":[93],"underexplored.":[94],"To":[95],"address":[96],"challenge,":[98],"our":[99],"study":[100],"comprehensively":[101],"analyzes":[102],"two":[103],"widely":[104],"used":[105],"mixing":[106],"on":[108],"three":[109,113],"prevalent":[110],"distinct":[114],"From":[117],"datasets,":[119],"we":[120],"sample":[121],"subsets":[122],"with":[123],"varying":[124],"proportions":[125],"investigate":[132],"impact":[134],"components.":[139],"The":[140],"findings":[141],"paper":[144],"provide":[145],"valuable":[146],"insights":[147],"into":[148],"optimizing":[149],"use":[151],"process":[158],"any":[160],"ANN,":[161],"contributing":[162],"enhancing":[164],"efficacy.":[167]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}