{"id":"https://openalex.org/W2972755454","doi":"https://doi.org/10.1145/3341162.3349330","title":"Rotation-equivariant convolutional neural network ensembles in image processing","display_name":"Rotation-equivariant convolutional neural network ensembles in image processing","publication_year":2019,"publication_date":"2019-09-09","ids":{"openalex":"https://openalex.org/W2972755454","doi":"https://doi.org/10.1145/3341162.3349330","mag":"2972755454"},"language":"en","primary_location":{"id":"doi:10.1145/3341162.3349330","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341162.3349330","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5011441236","display_name":"Liyao Gao","orcid":"https://orcid.org/0000-0001-9692-4264"},"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":true,"raw_author_name":"Liyao Gao","raw_affiliation_strings":["Purdue University"],"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110708520","display_name":"Hongshan Li","orcid":"https://orcid.org/0000-0002-8792-2774"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongshan Li","raw_affiliation_strings":["Insight Recruiting"],"affiliations":[{"raw_affiliation_string":"Insight Recruiting","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038009938","display_name":"Zheying Lu","orcid":null},"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":"Zheying Lu","raw_affiliation_strings":["Purdue University"],"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078138445","display_name":"Guang Lin","orcid":"https://orcid.org/0000-0002-0976-1987"},"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":"Guang Lin","raw_affiliation_strings":["Purdue University"],"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5011441236"],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":0.6073,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.72540532,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"551","last_page":"557"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.998199999332428,"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":0.998199999332428,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9980999827384949,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9969000220298767,"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/mnist-database","display_name":"MNIST database","score":0.9674139618873596},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8371385335922241},{"id":"https://openalex.org/keywords/rotation","display_name":"Rotation (mathematics)","score":0.7492762804031372},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7238528728485107},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6290205121040344},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6200342178344727},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5947586297988892},{"id":"https://openalex.org/keywords/equivariant-map","display_name":"Equivariant map","score":0.5535884499549866},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.49184536933898926},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4163588285446167},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21572566032409668}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.9674139618873596},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8371385335922241},{"id":"https://openalex.org/C74050887","wikidata":"https://www.wikidata.org/wiki/Q848368","display_name":"Rotation (mathematics)","level":2,"score":0.7492762804031372},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7238528728485107},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6290205121040344},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6200342178344727},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5947586297988892},{"id":"https://openalex.org/C171036898","wikidata":"https://www.wikidata.org/wiki/Q256355","display_name":"Equivariant map","level":2,"score":0.5535884499549866},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.49184536933898926},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4163588285446167},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21572566032409668},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3341162.3349330","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341162.3349330","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1490482062","https://openalex.org/W2100128988","https://openalex.org/W2150884987","https://openalex.org/W2163605009","https://openalex.org/W2569680626","https://openalex.org/W2769152880","https://openalex.org/W2785885194","https://openalex.org/W2805013548","https://openalex.org/W2963423708"],"related_works":["https://openalex.org/W2950475743","https://openalex.org/W4386603768","https://openalex.org/W2886711096","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W3155941649","https://openalex.org/W3036048022","https://openalex.org/W4309224979","https://openalex.org/W3026879719","https://openalex.org/W2798549588"],"abstract_inverted_index":{"For":[0],"the":[1,33,42,48,77,106,109],"present":[2,66],"engineering":[3],"of":[4,35,44,70,79,98,117,122],"neural":[5,36,45,72,86,92,134],"networks,":[6],"rotation":[7,99,119],"invariant":[8],"is":[9,16],"hard":[10],"to":[11,65,75,159],"be":[12],"obtained.":[13],"Rotation":[14],"symmetry":[15],"an":[17],"important":[18],"characteristic":[19],"in":[20,47,129],"our":[21,103],"physical":[22],"world.":[23],"In":[24,59,102],"image":[25,81],"recognition,":[26],"using":[27],"rotated":[28,80,141,146,152],"images":[29],"would":[30,63],"largely":[31],"decrease":[32],"performance":[34],"networks.":[37],"This":[38,84],"situation":[39],"seriously":[40],"hindered":[41],"application":[43],"networks":[46,93],"real-world,":[49],"such":[50],"as":[51],"human":[52],"tracking,":[53],"self-driving":[54],"cars,":[55],"and":[56,149],"intelligent":[57],"surveillance.":[58],"this":[60,130],"paper,":[61],"we":[62],"like":[64],"a":[67,123],"rotation-equivariant":[68],"design":[69],"convolutional":[71,85,91,133],"network":[73,87,135],"ensembles":[74,88,136],"counteract":[76],"problem":[78],"processing":[82],"task.":[83],"combine":[89],"multiple":[90],"trained":[94],"by":[95,112],"different":[96],"ranges":[97],"angles":[100,120],"respectively.":[101],"proposed":[104],"theory,":[105],"model":[107],"lowers":[108],"training":[110],"difficulty":[111],"learning":[113],"with":[114],"smaller":[115],"separations":[116],"random":[118],"instead":[121],"huge":[124],"one.":[125],"Experiments":[126],"are":[127,157],"reported":[128],"paper.":[131],"The":[132],"could":[137],"reach":[138],"96.35%":[139],"on":[140,145,151],"MNIST":[142],"datasets,":[143,148],"84.9%":[144],"Fashion-MNIST":[147],"91.35%":[150],"KMNIST":[153],"datasets.":[154],"These":[155],"results":[156],"comparable":[158],"current":[160],"state-of-the-art":[161],"performance.":[162]},"counts_by_year":[{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}