{"id":"https://openalex.org/W2997972888","doi":null,"title":"Large Scale Learning of General Visual Representations for Transfer.","display_name":"Large Scale Learning of General Visual Representations for Transfer.","publication_year":2019,"publication_date":"2019-12-24","ids":{"openalex":"https://openalex.org/W2997972888","mag":"2997972888"},"language":"en","primary_location":{"id":"mag:2997972888","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1912.11370v1","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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/1912.11370v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000026575","display_name":"Alexander Kolesnikov","orcid":"https://orcid.org/0009-0006-1133-6994","observed_orcids":["https://orcid.org/0009-0006-1133-6994"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alexander Kolesnikov","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055629014","display_name":"Lucas Beyer","orcid":"https://orcid.org/0000-0002-0460-0607","observed_orcids":["https://orcid.org/0000-0002-0460-0607"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lucas Beyer","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-0460-0607","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071668416","display_name":"Xiaohua Zhai","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaohua Zhai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001091279","display_name":"Joan Puigcerver","orcid":"https://orcid.org/0000-0002-1926-2233","observed_orcids":["https://orcid.org/0000-0002-1926-2233"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joan Puigcerver","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-1926-2233","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049532965","display_name":"Jessica Yung","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jessica Yung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041958651","display_name":"Sylvain Gelly","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sylvain Gelly","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5068878643","display_name":"Neil Houlsby","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Neil Houlsby","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":70,"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":1.0,"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":1.0,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.00019999999494757503,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.0,"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/visual-representation-learning","display_name":"visual representation learning","score":0.7049999833106995},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.8169999718666077},{"id":"https://openalex.org/keywords/pretraining","display_name":"pre-training","score":0.8709999918937683},{"id":"https://openalex.org/keywords/fine-tuning","display_name":"fine-tuning","score":0.8920000195503235},{"id":"https://openalex.org/keywords/small-sample-learning","display_name":"small sample learning","score":0.45899999141693115},{"id":"https://openalex.org/keywords/sample-efficiency","display_name":"sample efficiency","score":0.5220000147819519},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"hyperparameter optimization","score":0.5809999704360962},{"id":"https://openalex.org/keywords/imagenet","display_name":"ImageNet","score":0.4480000138282776},{"id":"https://openalex.org/keywords/cifar-10","display_name":"CIFAR-10","score":0.8999999761581421}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8409759402275085},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8117035627365112},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6839068531990051},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6614333391189575},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6394346356391907},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6382250785827637},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6198641657829285},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.5545852184295654},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5440340638160706},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.47541099786758423},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46861228346824646},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4663632810115814},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.4588606357574463},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.45132532715797424},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4254462420940399},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37621572613716125},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.08556994795799255},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2997972888","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1912.11370v1","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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:2997972888","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1912.11370v1","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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2100031962","https://openalex.org/W2108598243","https://openalex.org/W2117539524","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W2502312327","https://openalex.org/W2531409750","https://openalex.org/W2549139847","https://openalex.org/W2562153041","https://openalex.org/W2601450892","https://openalex.org/W2606722458","https://openalex.org/W2622263826","https://openalex.org/W2626017178","https://openalex.org/W2765407302","https://openalex.org/W2804935296","https://openalex.org/W2901299405","https://openalex.org/W2909869271","https://openalex.org/W2912218663","https://openalex.org/W2946948417","https://openalex.org/W2949117887","https://openalex.org/W2963341924","https://openalex.org/W2964105864","https://openalex.org/W2971315489","https://openalex.org/W2991391304","https://openalex.org/W2994268455","https://openalex.org/W3118608800"],"related_works":["https://openalex.org/W3015146382","https://openalex.org/W4285295542","https://openalex.org/W6959764124","https://openalex.org/W4415537371","https://openalex.org/W7125119745","https://openalex.org/W2799269579","https://openalex.org/W3131254173","https://openalex.org/W7221682522","https://openalex.org/W3190434573","https://openalex.org/W3171483789"],"abstract_inverted_index":{"Transfer":[0,49],"of":[1,22,80,140],"pre-trained":[2],"representations":[3],"improves":[4],"sample":[5],"efficiency":[6],"and":[7,28,40,58,103,127],"simplifies":[8],"hyperparameter":[9],"tuning":[10],"when":[11],"training":[12],"deep":[13],"neural":[14],"networks":[15],"for":[16],"vision.":[17],"We":[18,36,136],"revisit":[19],"the":[20,30,106,141],"paradigm":[21],"pre-training":[23],"on":[24,32,68,98,101,105,120,129],"large":[25],"supervised":[26],"datasets":[27],"fine-tuning":[29],"model":[31],"a":[33,42,53,61,76],"target":[34],"task.":[35],"scale":[37],"up":[38],"pre-training,":[39],"propose":[41],"simple":[43,62],"recipe":[44],"that":[45,144],"we":[46,64],"call":[47],"Big":[48],"(BiT).":[50],"By":[51],"combining":[52],"few":[54],"carefully":[55],"selected":[56],"components,":[57],"transferring":[59],"using":[60],"heuristic,":[63],"achieve":[65],"strong":[66],"performance":[67],"over":[69],"20":[70],"datasets.":[71],"BiT":[72,93,117],"performs":[73],"well":[74],"across":[75],"surprisingly":[77],"wide":[78],"range":[79],"data":[81],"regimes":[82],"--":[83],"from":[84],"1":[85],"example":[86],"per":[87,125,134],"class":[88],"to":[89,146],"1M":[90],"total":[91],"examples.":[92],"achieves":[94],"87.5%":[95],"top-1":[96],"accuracy":[97],"ILSVRC-2012,":[99],"99.4%":[100],"CIFAR-10,":[102],"76.3%":[104],"19":[107],"task":[108],"Visual":[109],"Task":[110],"Adaptation":[111],"Benchmark":[112],"(VTAB).":[113],"On":[114],"small":[115],"datasets,":[116],"attains":[118],"76.8%":[119],"ILSVRC-2012":[121],"with":[122,131],"10":[123,132],"examples":[124,133],"class,":[126],"97.0%":[128],"CIFAR-10":[130],"class.":[135],"conduct":[137],"detailed":[138],"analysis":[139],"main":[142],"components":[143],"lead":[145],"high":[147],"transfer":[148],"performance.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":29}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2020-01-10T00:00:00"}