{"id":"https://openalex.org/W4312091908","doi":"https://doi.org/10.48550/arxiv.2212.10553","title":"RangeAugment: Efficient Online Augmentation with Range Learning","display_name":"RangeAugment: Efficient Online Augmentation with Range Learning","publication_year":2022,"publication_date":"2022-12-20","ids":{"openalex":"https://openalex.org/W4312091908","doi":"https://doi.org/10.48550/arxiv.2212.10553"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2212.10553","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.10553","pdf_url":"https://arxiv.org/pdf/2212.10553","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":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2212.10553","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074132108","display_name":"Sachin Mehta","orcid":"https://orcid.org/0000-0002-5420-4725","observed_orcids":["https://orcid.org/0000-0002-5420-4725"]},"institutions":[{"id":"https://openalex.org/I4210153776","display_name":"Apple (United States)","ror":"https://ror.org/059hsda18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153776"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mehta, Sachin","raw_affiliation_strings":["Apple","Project lead and main"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Apple","institution_ids":["https://openalex.org/I4210153776"]},{"raw_affiliation_string":"Project lead and main","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067431517","display_name":"Saeid Naderiparizi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Naderiparizi, Saeid","raw_affiliation_strings":["University of British Columbia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of British Columbia","institution_ids":["https://openalex.org/I141945490"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036601505","display_name":"Fartash Faghri","orcid":"https://orcid.org/0000-0001-5975-5158","observed_orcids":["https://orcid.org/0000-0001-5975-5158"]},"institutions":[{"id":"https://openalex.org/I4210153776","display_name":"Apple (United States)","ror":"https://ror.org/059hsda18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153776"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Faghri, Fartash","raw_affiliation_strings":["Apple"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Apple","institution_ids":["https://openalex.org/I4210153776"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012428670","display_name":"Maxwell Horton","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210153776","display_name":"Apple (United States)","ror":"https://ror.org/059hsda18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153776"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Horton, Maxwell","raw_affiliation_strings":["Apple"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Apple","institution_ids":["https://openalex.org/I4210153776"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039174435","display_name":"Lailin Chen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210153776","display_name":"Apple (United States)","ror":"https://ror.org/059hsda18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153776"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen, Lailin","raw_affiliation_strings":["Apple"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Apple","institution_ids":["https://openalex.org/I4210153776"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101576595","display_name":"Ali Farhadi","orcid":"https://orcid.org/0000-0001-7249-2380","observed_orcids":["https://orcid.org/0000-0001-7249-2380"]},"institutions":[{"id":"https://openalex.org/I4210153776","display_name":"Apple (United States)","ror":"https://ror.org/059hsda18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153776"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Farhadi, Ali","raw_affiliation_strings":["Apple"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Apple","institution_ids":["https://openalex.org/I4210153776"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028613002","display_name":"Oncel Tuzel","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210153776","display_name":"Apple (United States)","ror":"https://ror.org/059hsda18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153776"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tuzel, Oncel","raw_affiliation_strings":["Apple"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Apple","institution_ids":["https://openalex.org/I4210153776"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056246621","display_name":"Mohammad Rastegari","orcid":"https://orcid.org/0000-0001-9606-3687","observed_orcids":["https://orcid.org/0000-0001-9606-3687"]},"institutions":[{"id":"https://openalex.org/I4210153776","display_name":"Apple (United States)","ror":"https://ror.org/059hsda18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153776"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rastegari, Mohammad","raw_affiliation_strings":["Apple"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Apple","institution_ids":["https://openalex.org/I4210153776"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.482699990272522,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.482699990272522,"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":0.302700012922287,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.18860000371932983,"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/linear-search","display_name":"sequential search","score":0.5770000219345093},{"id":"https://openalex.org/keywords/autoaugment","display_name":"AutoAugment","score":0.6140000224113464},{"id":"https://openalex.org/keywords/randaugment","display_name":"RandAugment","score":0.9959999918937683},{"id":"https://openalex.org/keywords/visual-recognition","display_name":"visual recognition","score":0.578000009059906},{"id":"https://openalex.org/keywords/semantic-segmentation","display_name":"semantic segmentation","score":0.8999999761581421},{"id":"https://openalex.org/keywords/object-detection","display_name":"object detection","score":0.9760000109672546},{"id":"https://openalex.org/keywords/knowledge-distillation","display_name":"knowledge distillation","score":0.7770000100135803},{"id":"https://openalex.org/keywords/foundation-models","display_name":"foundation models","score":0.890999972820282}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7398108839988708},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.6806188821792603},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5546801090240479},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.526390552520752},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4928562641143799},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4623628258705139},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4292646646499634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4029155671596527},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3363112807273865},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2212.10553","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.10553","pdf_url":"https://arxiv.org/pdf/2212.10553","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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2212.10553","doi":"https://doi.org/10.48550/arxiv.2212.10553","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2212.10553","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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2212.10553","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.10553","pdf_url":"https://arxiv.org/pdf/2212.10553","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":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2993466051","https://openalex.org/W2975761646","https://openalex.org/W2804047946","https://openalex.org/W3009184501","https://openalex.org/W7160645783","https://openalex.org/W2899740757","https://openalex.org/W4287265555","https://openalex.org/W4372273308","https://openalex.org/W2949736877","https://openalex.org/W2972348904"],"abstract_inverted_index":{"State-of-the-art":[0],"automatic":[1,168],"augmentation":[2,20,28,72,93,114,147,169,175],"methods":[3,43,170],"(e.g.,":[4,30],"AutoAugment":[5],"and":[6,32,46,142,145,186],"RandAugment)":[7],"for":[8,50,70,87,125],"visual":[9],"recognition":[10],"tasks":[11],"diversify":[12],"training":[13],"data":[14],"using":[15],"a":[16,105,117,121],"large":[17],"set":[18],"of":[19,24,26,67,85,111,113],"operations.":[21,94,115,176],"The":[22],"range":[23,84,110],"magnitudes":[25,86,112],"many":[27],"operations":[29],"brightness":[31],"contrast)":[33],"is":[34],"continuous.":[35],"Therefore,":[36],"to":[37,56,80,107,166],"make":[38],"search":[39],"computationally":[40],"tractable,":[41],"these":[42],"use":[44],"fixed":[45],"manually-defined":[47],"magnitude":[48,68],"ranges":[49,69],"each":[51,71],"operation,":[52,73],"which":[53,129],"may":[54],"lead":[55],"sub-optimal":[57],"policies.":[58,148],"To":[59],"answer":[60],"the":[61,65,83,109,153],"open":[62],"question":[63],"on":[64,101,152,179],"importance":[66],"we":[74,130,159],"introduce":[75],"RangeAugment":[76,95,119,136,162],"that":[77,161],"allows":[78],"us":[79],"efficiently":[81],"learn":[82],"individual":[88],"as":[89,91,104],"well":[90],"composite":[92],"uses":[96],"an":[97],"auxiliary":[98],"loss":[99],"based":[100],"image":[102,127],"similarity":[103],"measure":[106],"control":[108],"As":[116],"result,":[118],"has":[120],"single":[122],"scalar":[123],"parameter":[124],"search,":[126],"similarity,":[128],"simply":[131],"optimize":[132],"via":[133],"linear":[134],"search.":[135],"integrates":[137],"seamlessly":[138],"with":[139,171],"any":[140],"model":[141],"learns":[143],"model-":[144],"task-specific":[146],"With":[149],"extensive":[150],"experiments":[151],"ImageNet":[154],"dataset":[155],"across":[156],"different":[157],"networks,":[158],"show":[160],"achieves":[163],"competitive":[164],"performance":[165],"state-of-the-art":[167],"4-5":[172],"times":[173],"fewer":[174],"Experimental":[177],"results":[178],"semantic":[180],"segmentation,":[181],"object":[182],"detection,":[183],"foundation":[184],"models,":[185],"knowledge":[187],"distillation":[188],"further":[189],"shows":[190],"RangeAugment's":[191],"effectiveness.":[192]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2023-01-04T00:00:00"}