{"id":"https://openalex.org/W3215503755","doi":"https://doi.org/10.1109/tnnls.2021.3117685","title":"LRPRNet: Lightweight Deep Network by Low-Rank Pointwise Residual Convolution","display_name":"LRPRNet: Lightweight Deep Network by Low-Rank Pointwise Residual Convolution","publication_year":2021,"publication_date":"2021-11-30","ids":{"openalex":"https://openalex.org/W3215503755","doi":"https://doi.org/10.1109/tnnls.2021.3117685","mag":"3215503755","pmid":"https://pubmed.ncbi.nlm.nih.gov/34847044"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2021.3117685","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3117685","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Neural Networks and Learning Systems","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":null,"display_name":"Bin Sun","orcid":"https://orcid.org/0000-0001-9239-0402"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bin Sun","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA"],"raw_orcid":"https://orcid.org/0000-0001-9239-0402","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jun Li","orcid":"https://orcid.org/0000-0001-5845-8602"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Li","raw_affiliation_strings":["Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA"],"raw_orcid":"https://orcid.org/0000-0001-5845-8602","affiliations":[{"raw_affiliation_string":"Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ming Shao","orcid":"https://orcid.org/0000-0002-7686-8784"},"institutions":[{"id":"https://openalex.org/I100633361","display_name":"University of Massachusetts Dartmouth","ror":"https://ror.org/00fzmm222","country_code":"US","type":"education","lineage":["https://openalex.org/I100633361"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ming Shao","raw_affiliation_strings":["Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-7686-8784","affiliations":[{"raw_affiliation_string":"Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA","institution_ids":["https://openalex.org/I100633361"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yun Fu","orcid":"https://orcid.org/0000-0002-5098-2853"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yun Fu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-5098-2853","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7537,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.73751847,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"34","issue":"8","first_page":"4440","last_page":"4450"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.30169999599456787,"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":0.30169999599456787,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.1185000017285347,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.11020000278949738,"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/pointwise","display_name":"Pointwise","score":0.8704000115394592},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.75},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7342000007629395},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7330999970436096},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.5407999753952026},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5044999718666077},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4343000054359436}],"concepts":[{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.8704000115394592},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.75},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7342000007629395},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7330999970436096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.699999988079071},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5813999772071838},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.5407999753952026},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5044999718666077},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4343000054359436},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4293000102043152},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4284000098705292},{"id":"https://openalex.org/C21442007","wikidata":"https://www.wikidata.org/wiki/Q1027879","display_name":"Graphics","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4205000102519989},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32019999623298645},{"id":"https://openalex.org/C77660652","wikidata":"https://www.wikidata.org/wiki/Q150971","display_name":"Computer graphics","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.29649999737739563},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.29339998960494995},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.28209999203681946},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27570000290870667}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2021.3117685","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3117685","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:34847044","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34847044","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 neural networks and learning systems","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":73,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W1976948919","https://openalex.org/W1990937109","https://openalex.org/W1996901117","https://openalex.org/W1998294030","https://openalex.org/W2038952578","https://openalex.org/W2087681821","https://openalex.org/W2097117768","https://openalex.org/W2108598243","https://openalex.org/W2117539524","https://openalex.org/W2136000821","https://openalex.org/W2138019504","https://openalex.org/W2151802865","https://openalex.org/W2152826865","https://openalex.org/W2157285372","https://openalex.org/W2194775991","https://openalex.org/W2300242332","https://openalex.org/W2474575620","https://openalex.org/W2531409750","https://openalex.org/W2559085405","https://openalex.org/W2736728583","https://openalex.org/W2752197020","https://openalex.org/W2778955544","https://openalex.org/W2783000019","https://openalex.org/W2832876791","https://openalex.org/W2949662773","https://openalex.org/W2962697884","https://openalex.org/W2962772649","https://openalex.org/W2962861284","https://openalex.org/W2962890819","https://openalex.org/W2963125010","https://openalex.org/W2963202462","https://openalex.org/W2963216524","https://openalex.org/W2963253045","https://openalex.org/W2963377935","https://openalex.org/W2963446712","https://openalex.org/W2963821229","https://openalex.org/W2963918968","https://openalex.org/W2963993763","https://openalex.org/W2964014798","https://openalex.org/W2964081807","https://openalex.org/W2964130064","https://openalex.org/W2964350391","https://openalex.org/W2967733054","https://openalex.org/W2982083293","https://openalex.org/W2999241864","https://openalex.org/W3005165565","https://openalex.org/W3025665229","https://openalex.org/W3048300167","https://openalex.org/W3128628613","https://openalex.org/W3185946996","https://openalex.org/W4236868170","https://openalex.org/W6631190155","https://openalex.org/W6637078681","https://openalex.org/W6637373629","https://openalex.org/W6638523607","https://openalex.org/W6638667902","https://openalex.org/W6679667936","https://openalex.org/W6682889407","https://openalex.org/W6684191040","https://openalex.org/W6686509673","https://openalex.org/W6695314431","https://openalex.org/W6711957290","https://openalex.org/W6737664043","https://openalex.org/W6745148473","https://openalex.org/W6751763797","https://openalex.org/W6751979845","https://openalex.org/W6752515464","https://openalex.org/W6753767121","https://openalex.org/W6754943526","https://openalex.org/W6756887525","https://openalex.org/W6766029800","https://openalex.org/W6793831464"],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning":[1,52],"has":[2],"become":[3],"popular":[4,138],"in":[5,70],"recent":[6],"years":[7],"primarily":[8],"due":[9],"to":[10,24,29,73,87,113,158],"powerful":[11],"computing":[12],"devices":[13],"such":[14,118],"as":[15,83,119],"graphics":[16],"processing":[17],"units":[18],"(GPUs).":[19],"However,":[20],"it":[21],"is":[22,93,105],"challenging":[23],"deploy":[25],"these":[26],"deep":[27,51,161],"models":[28],"multimedia":[30],"devices,":[31],"smartphones,":[32],"or":[33],"embedded":[34],"systems":[35],"with":[36,148],"limited":[37],"resources.":[38],"To":[39],"reduce":[40,75],"the":[41,76,84,89,96,99,159],"computation":[42],"and":[43,108,123,134,154],"memory":[44,155],"costs,":[45],"we":[46],"propose":[47],"a":[48,67,149],"novel":[49],"lightweight":[50,162],"module":[53,77,86],"by":[54],"low-rank":[55,68],"pointwise":[56,71],"residual":[57,85],"(LRPR)":[58],"convolution,":[59],"called":[60],"LRPRNet.":[61],"Essentially,":[62],"LRPR":[63,90,104],"aims":[64],"at":[65],"using":[66],"approximation":[69],"convolution":[72,100],"further":[74],"size":[78],"while":[79],"keeping":[80],"depthwise":[81],"convolutions":[82],"rectify":[88],"module.":[91],"This":[92],"critical":[94],"when":[95],"low-rankness":[97],"undermines":[98],"process.":[101],"Moreover,":[102],"our":[103,142],"quite":[106],"general":[107],"can":[109],"be":[110],"directly":[111],"applied":[112],"many":[114],"existing":[115],"network":[116],"architectures":[117],"MobileNetv1,":[120],"ShuffleNetv2,":[121],"MixNet,":[122],"so":[124],"on.":[125],"Experiments":[126],"on":[127,137],"visual":[128],"recognition":[129],"tasks,":[130],"including":[131],"image":[132],"classification":[133],"face":[135],"alignment":[136],"benchmarks,":[139],"show":[140],"that":[141],"LRPRNet":[143],"achieves":[144],"competitive":[145],"performance":[146],"but":[147],"significant":[150],"reduction":[151],"of":[152],"Flops":[153],"cost":[156],"compared":[157],"state-of-the-art":[160],"models.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2021-12-06T00:00:00"}