{"id":"https://openalex.org/W4307306392","doi":"https://doi.org/10.48550/arxiv.2206.00051","title":"Learning Instance-Specific Augmentations by Capturing Local Invariances","display_name":"Learning Instance-Specific Augmentations by Capturing Local Invariances","publication_year":2022,"publication_date":"2022-05-31","ids":{"openalex":"https://openalex.org/W4307306392","doi":"https://doi.org/10.48550/arxiv.2206.00051"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2206.00051","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.00051","pdf_url":"https://arxiv.org/pdf/2206.00051","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/2206.00051","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090188393","display_name":"Ning Miao","orcid":"https://orcid.org/0000-0002-1783-5827"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Miao, Ning","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078631467","display_name":"Tom Rainforth","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rainforth, Tom","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051016788","display_name":"\u00c9mile Mathieu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mathieu, Emile","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032176444","display_name":"Yann Dubois","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dubois, Yann","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064373793","display_name":"Yee Whye Teh","orcid":"https://orcid.org/0000-0001-5365-6933"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Teh, Yee Whye","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030321890","display_name":"Adam S. Foster","orcid":"https://orcid.org/0000-0001-5371-5905"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Foster, Adam","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009518283","display_name":"Hyunjik Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Hyunjik","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5090188393"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"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":0.9954000115394592,"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":0.9954000115394592,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9919000267982483,"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":0.9829999804496765,"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/computer-science","display_name":"Computer science","score":0.7861440181732178},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.758404016494751},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.6257686614990234},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5994890332221985},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5474473237991333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3680298924446106},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32129132747650146},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18085765838623047}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7861440181732178},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.758404016494751},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.6257686614990234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5994890332221985},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5474473237991333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3680298924446106},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32129132747650146},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18085765838623047},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2206.00051","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.00051","pdf_url":"https://arxiv.org/pdf/2206.00051","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.2206.00051","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2206.00051","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2206.00051","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.00051","pdf_url":"https://arxiv.org/pdf/2206.00051","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3125750421","https://openalex.org/W68147753","https://openalex.org/W2949765904","https://openalex.org/W4306949324","https://openalex.org/W4388544318","https://openalex.org/W618363683","https://openalex.org/W1978104062","https://openalex.org/W4281657016","https://openalex.org/W594289152","https://openalex.org/W636031027"],"abstract_inverted_index":{"We":[0,94],"introduce":[1],"InstaAug,":[2],"a":[3,55,83,91,104],"method":[4],"for":[5,14,90,103],"automatically":[6],"learning":[7,15],"input-specific":[8],"augmentations":[9,16,102],"from":[10,61],"data.":[11],"Previous":[12],"methods":[13],"have":[17],"typically":[18],"assumed":[19],"independence":[20],"between":[21],"the":[22,26,38,79],"original":[23],"input":[24,50],"and":[25,119],"transformation":[27,65,108],"applied":[28],"to":[29,63,70],"that":[30,59,97],"input.":[31],"This":[32,73],"can":[33,74],"be":[34,71,75],"highly":[35,49],"restrictive,":[36],"as":[37],"invariances":[39,69],"we":[40],"hope":[41],"our":[42],"augmentation":[43],"will":[44],"capture":[45],"are":[46],"themselves":[47],"often":[48],"dependent.":[51],"InstaAug":[52,98],"instead":[53],"introduces":[54],"learnable":[56],"invariance":[57],"module":[58],"maps":[60],"inputs":[62],"tailored":[64],"parameters,":[66],"allowing":[67],"local":[68],"captured.":[72],"simultaneously":[76],"trained":[77],"alongside":[78],"downstream":[80],"model":[81],"in":[82,111],"fully":[84],"end-to-end":[85],"manner,":[86],"or":[87],"separately":[88],"learned":[89],"pre-trained":[92],"model.":[93],"empirically":[95],"demonstrate":[96],"learns":[99],"meaningful":[100],"input-dependent":[101],"wide":[105],"range":[106],"of":[107],"classes,":[109],"which":[110],"turn":[112],"provides":[113],"better":[114],"performance":[115],"on":[116],"both":[117],"supervised":[118],"self-supervised":[120],"tasks.":[121]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
