{"id":"https://openalex.org/W3092095626","doi":"https://doi.org/10.1109/tpami.2020.3029801","title":"Learning Generalized Transformation Equivariant Representations Via AutoEncoding Transformations","display_name":"Learning Generalized Transformation Equivariant Representations Via AutoEncoding Transformations","publication_year":2020,"publication_date":"2020-10-09","ids":{"openalex":"https://openalex.org/W3092095626","doi":"https://doi.org/10.1109/tpami.2020.3029801","mag":"3092095626","pmid":"https://pubmed.ncbi.nlm.nih.gov/33035159"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2020.3029801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2020.3029801","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100766907","display_name":"Guo-Jun Qi","orcid":"https://orcid.org/0000-0003-3508-1851"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Guo-Jun Qi","raw_affiliation_strings":["Futurewei Seattle Cloud Lab, Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Futurewei Seattle Cloud Lab, Seattle, WA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101953053","display_name":"Liheng Zhang","orcid":"https://orcid.org/0000-0002-7743-6959"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liheng Zhang","raw_affiliation_strings":["Department of Computer Science, University of Central Florida, Orlando, FL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101904898","display_name":"Feng Lin","orcid":"https://orcid.org/0000-0001-8541-7400"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Lin","raw_affiliation_strings":["Department of Electronic Information Engineering, University of Science and Technology of China, Hefei, Anhui, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Information Engineering, University of Science and Technology of China, Hefei, Anhui, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100411532","display_name":"Xiao Wang","orcid":"https://orcid.org/0000-0003-4435-7098"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiao Wang","raw_affiliation_strings":["Department of Computer Science, Purdue University, West Lafayette, IN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100766907"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.5071,"has_fulltext":false,"cited_by_count":46,"citation_normalized_percentile":{"value":0.95513553,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"44","issue":"4","first_page":"2045","last_page":"2057"},"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":0.9995999932289124,"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":0.9995999932289124,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9993000030517578,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9993000030517578,"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/equivariant-map","display_name":"Equivariant map","score":0.7560637593269348},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.702922523021698},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6903671026229858},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6665400266647339},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.658816933631897},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.537613034248352},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4946286380290985},{"id":"https://openalex.org/keywords/geometric-transformation","display_name":"Geometric transformation","score":0.4436117112636566},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.4228397607803345},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.41739416122436523},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4137493968009949},{"id":"https://openalex.org/keywords/rigid-transformation","display_name":"Rigid transformation","score":0.4136850833892822},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3350070118904114},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2711883783340454},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21994167566299438}],"concepts":[{"id":"https://openalex.org/C171036898","wikidata":"https://www.wikidata.org/wiki/Q256355","display_name":"Equivariant map","level":2,"score":0.7560637593269348},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.702922523021698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6903671026229858},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6665400266647339},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.658816933631897},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.537613034248352},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4946286380290985},{"id":"https://openalex.org/C56435381","wikidata":"https://www.wikidata.org/wiki/Q1196371","display_name":"Geometric transformation","level":3,"score":0.4436117112636566},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.4228397607803345},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.41739416122436523},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4137493968009949},{"id":"https://openalex.org/C126795593","wikidata":"https://www.wikidata.org/wiki/Q7333813","display_name":"Rigid transformation","level":2,"score":0.4136850833892822},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3350070118904114},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2711883783340454},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21994167566299438},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2020.3029801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2020.3029801","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:33035159","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33035159","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":84,"referenced_works":["https://openalex.org/W2966661","https://openalex.org/W115285041","https://openalex.org/W219040644","https://openalex.org/W343636949","https://openalex.org/W1520997877","https://openalex.org/W1536680647","https://openalex.org/W1932922755","https://openalex.org/W1959608418","https://openalex.org/W1972719705","https://openalex.org/W2025768430","https://openalex.org/W2028017042","https://openalex.org/W2050398567","https://openalex.org/W2052530028","https://openalex.org/W2066440363","https://openalex.org/W2067042811","https://openalex.org/W2084968769","https://openalex.org/W2094067871","https://openalex.org/W2096987362","https://openalex.org/W2164122462","https://openalex.org/W2287334441","https://openalex.org/W2321533354","https://openalex.org/W2326925005","https://openalex.org/W2335728318","https://openalex.org/W2464503653","https://openalex.org/W2473499128","https://openalex.org/W2474309084","https://openalex.org/W2512563520","https://openalex.org/W2558661413","https://openalex.org/W2603142085","https://openalex.org/W2607510315","https://openalex.org/W2618530766","https://openalex.org/W2750549109","https://openalex.org/W2785325870","https://openalex.org/W2798991696","https://openalex.org/W2807754035","https://openalex.org/W2883725317","https://openalex.org/W2890179236","https://openalex.org/W2894573160","https://openalex.org/W2895115565","https://openalex.org/W2951970475","https://openalex.org/W2962808998","https://openalex.org/W2963192800","https://openalex.org/W2963420272","https://openalex.org/W2963584589","https://openalex.org/W2963684088","https://openalex.org/W2964159205","https://openalex.org/W2964314455","https://openalex.org/W2982247743","https://openalex.org/W2982376094","https://openalex.org/W2990721113","https://openalex.org/W2991327923","https://openalex.org/W2998388430","https://openalex.org/W2998569746","https://openalex.org/W3005680577","https://openalex.org/W3009561768","https://openalex.org/W3118608800","https://openalex.org/W4294568686","https://openalex.org/W6620707391","https://openalex.org/W6640963894","https://openalex.org/W6675401909","https://openalex.org/W6679792166","https://openalex.org/W6682250724","https://openalex.org/W6685352114","https://openalex.org/W6688386640","https://openalex.org/W6694954494","https://openalex.org/W6703116779","https://openalex.org/W6714590955","https://openalex.org/W6715501732","https://openalex.org/W6718379498","https://openalex.org/W6733814495","https://openalex.org/W6736894310","https://openalex.org/W6743446608","https://openalex.org/W6744627333","https://openalex.org/W6747899497","https://openalex.org/W6748053814","https://openalex.org/W6752772801","https://openalex.org/W6764051988","https://openalex.org/W6770725473","https://openalex.org/W6772112314","https://openalex.org/W6774314701","https://openalex.org/W6774670964","https://openalex.org/W6779669310","https://openalex.org/W6780360855","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2027919853","https://openalex.org/W2142750822","https://openalex.org/W1998296102","https://openalex.org/W2049631777","https://openalex.org/W2123655559","https://openalex.org/W4226373316","https://openalex.org/W4387928664","https://openalex.org/W2112548940","https://openalex.org/W2014035569","https://openalex.org/W2287511726"],"abstract_inverted_index":{"Transformation":[0],"equivariant":[1,93],"representations":[2,51],"(TERs)":[3],"aim":[4],"to":[5,13,48,123],"capture":[6],"the":[7,18,24,58,65,68,71,77,82,109,129,133,143,147,160,166,178],"intrinsic":[8],"visual":[9,50,106],"structures":[10,107],"that":[11,159],"equivary":[12],"various":[14],"transformations":[15,66,95],"by":[16,62,75,101,126],"expanding":[17],"notion":[19],"of":[20,26,55,105,132],"translation":[21],"equivariance":[22,112],"underlying":[23],"success":[25],"convolutional":[27],"neural":[28],"networks":[29],"(CNNs).":[30],"For":[31],"this":[32],"purpose,":[33],"we":[34,157],"present":[35],"both":[36,137,151],"deterministic":[37],"AutoEncoding":[38,43],"Transformations":[39,45],"(AET)":[40],"and":[41,85,139,153],"probabilistic":[42],"Variational":[44],"(AVT)":[46],"models":[47,125,145,149],"learn":[49],"from":[52,67],"generic":[53],"groups":[54],"transformations.":[56,86,140],"While":[57],"AET":[59],"is":[60,73],"trained":[61,74],"directly":[63],"decoding":[64],"learned":[69,83,134],"representations,":[70],"AVT":[72],"maximizing":[76,128],"joint":[78],"mutual":[79,130],"information":[80,131],"between":[81],"representation":[84,135,162,169],"This":[87],"results":[88],"in":[89,96,150],"generalized":[90],"TERs":[91],"(GTERs)":[92],"against":[94],"a":[97,114],"more":[98],"general":[99],"fashion":[100],"capturing":[102],"complex":[103],"patterns":[104],"beyond":[108],"conventional":[110],"linear":[111],"under":[113],"transformation":[115],"group.":[116],"The":[117],"presented":[118],"approach":[119],"can":[120,163],"be":[121],"extended":[122],"(semi-)supervised":[124,154],"jointly":[127],"with":[136],"labels":[138],"Experiments":[141],"demonstrate":[142],"proposed":[144],"outperform":[146],"state-of-the-art":[148],"unsupervised":[152,161],"tasks.":[155],"Moreover,":[156],"show":[158],"even":[164],"surpass":[165],"fully":[167],"supervised":[168],"pretrained":[170],"on":[171],"ImageNet":[172],"when":[173],"they":[174],"are":[175],"fine-tuned":[176],"for":[177],"object":[179],"detection":[180],"task.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}