{"id":"https://openalex.org/W4221146488","doi":"https://doi.org/10.48550/arxiv.2203.04600","title":"Domain Generalization using Pretrained Models without Fine-tuning","display_name":"Domain Generalization using Pretrained Models without Fine-tuning","publication_year":2022,"publication_date":"2022-03-09","ids":{"openalex":"https://openalex.org/W4221146488","doi":"https://doi.org/10.48550/arxiv.2203.04600"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2203.04600","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.04600","pdf_url":"https://arxiv.org/pdf/2203.04600","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2203.04600","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100739060","display_name":"Ziyue Li","orcid":"https://orcid.org/0000-0001-9901-432X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Ziyue","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059778636","display_name":"Kan Ren","orcid":"https://orcid.org/0000-0003-3391-5795"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Kan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011424573","display_name":"Xinyang Jiang","orcid":"https://orcid.org/0000-0002-4991-0596"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Xinyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374360","display_name":"Bo Li","orcid":"https://orcid.org/0000-0001-6709-0942"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Bo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100758732","display_name":"Haipeng Zhang","orcid":"https://orcid.org/0000-0001-5741-2311"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Haipeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100440903","display_name":"Dongsheng Li","orcid":"https://orcid.org/0000-0001-9743-2034"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Dongsheng","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100739060"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":18,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.996999979019165,"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":0.996999979019165,"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":0.9746999740600586,"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/leverage","display_name":"Leverage (statistics)","score":0.8322415947914124},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7830651998519897},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7691999673843384},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6090065240859985},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5319054126739502},{"id":"https://openalex.org/keywords/adapter","display_name":"Adapter (computing)","score":0.5034241080284119},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.41490256786346436},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10623770952224731}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.8322415947914124},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7830651998519897},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7691999673843384},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6090065240859985},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5319054126739502},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.5034241080284119},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.41490256786346436},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10623770952224731},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2203.04600","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.04600","pdf_url":"https://arxiv.org/pdf/2203.04600","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2203.04600","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2203.04600","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2203.04600","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.04600","pdf_url":"https://arxiv.org/pdf/2203.04600","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2133028525","https://openalex.org/W4306381730","https://openalex.org/W4229060448","https://openalex.org/W2981692913","https://openalex.org/W3044188621","https://openalex.org/W3184035966","https://openalex.org/W2160602540","https://openalex.org/W2485605994","https://openalex.org/W4385571108","https://openalex.org/W4200251711"],"abstract_inverted_index":{"Fine-tuning":[0],"pretrained":[1,24,46,69,88,107,127,135,159],"models":[2,47,70,89,160],"is":[3,14,153],"a":[4,98,120],"common":[5],"practice":[6],"in":[7,42,90,185],"domain":[8,34,100,114],"generalization":[9,38,52,66,101,115],"(DG)":[10],"tasks.":[11,92],"However,":[12,64],"fine-tuning":[13],"usually":[15],"computationally":[16],"expensive":[17],"due":[18],"to":[19,85,103,137,155,161,179],"the":[20,65,131,134,138,142,190,196],"ever-growing":[21],"size":[22],"of":[23,51,68,133,141,150],"models.":[25],"More":[26],"importantly,":[27],"it":[28],"may":[29],"cause":[30],"over-fitting":[31],"on":[32,168],"source":[33],"and":[35,54,62,188,195],"compromise":[36],"their":[37],"ability":[39,53],"as":[40],"shown":[41],"recent":[43],"works.":[44],"Generally,":[45],"possess":[48],"some":[49],"level":[50],"can":[55],"achieve":[56],"decent":[57],"performance":[58,67,176],"regarding":[59],"specific":[60],"domains":[61,77],"samples.":[63],"could":[71],"vary":[72],"significantly":[73],"over":[74],"different":[75],"test":[76,164],"even":[78],"samples,":[79],"which":[80,129],"raises":[81],"challenges":[82],"for":[83,113],"us":[84],"best":[86],"leverage":[87,105],"DG":[91,186],"In":[93],"this":[94],"paper,":[95],"we":[96],"propose":[97],"novel":[99],"paradigm":[102],"better":[104],"various":[106],"models,":[108,128],"named":[109],"specialized":[110],"ensemble":[111,147],"learning":[112],"(SEDGE).":[116],"It":[117],"first":[118],"trains":[119],"linear":[121],"label":[122,139],"space":[123,140],"adapter":[124],"upon":[125],"fixed":[126],"transforms":[130],"outputs":[132],"model":[136,151],"target":[143],"domain.":[144],"Then,":[145],"an":[146],"network":[148],"aware":[149],"specialty":[152],"proposed":[154],"dynamically":[156],"dispatch":[157],"proper":[158],"predict":[162],"each":[163],"sample.":[165],"Experimental":[166],"studies":[167],"several":[169],"benchmarks":[170],"show":[171],"that":[172],"SEDGE":[173],"achieves":[174],"significant":[175],"improvements":[177],"comparing":[178],"strong":[180],"baselines":[181],"including":[182],"state-of-the-art":[183],"method":[184],"tasks":[187],"reduces":[189],"trainable":[191],"parameters":[192],"by":[193,199],"~99%":[194],"training":[197],"time":[198],"~99.5%.":[200]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
