iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.openalex.org/works/doi:10.48550/ARXIV.2212.00272
{"id":"https://openalex.org/W4310629432","doi":"https://doi.org/10.48550/arxiv.2212.00272","title":"ResNet Structure Simplification with the Convolutional Kernel Redundancy Measure","display_name":"ResNet Structure Simplification with the Convolutional Kernel Redundancy Measure","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4310629432","doi":"https://doi.org/10.48550/arxiv.2212.00272"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2212.00272","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":["Cornell University"],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2212.00272","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100568263","display_name":"Hongzhi Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Hongzhi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088844445","display_name":"Robert Rohling","orcid":"https://orcid.org/0000-0001-9026-8147"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rohling, Robert","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5028375560","display_name":"Septimiu E. Salcudean","orcid":"https://orcid.org/0000-0001-8826-8025"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Salcudean, Septimiu","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":60},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9987,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9987,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9977,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9966,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.6884508},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5803646}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.79472196},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.7716582},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.76364183},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6909561},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.6884508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6212658},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5803646},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.48687664},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46549356},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38933158},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.37897408},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.269734},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0899843},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2212.00272","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":["Cornell University"],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.00272","pdf_url":"http://arxiv.org/pdf/2212.00272","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2212.00272","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2212.00272","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":["Cornell University"],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.45,"display_name":"Industry, innovation and infrastructure"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4360783045","https://openalex.org/W4323057981","https://openalex.org/W4310870954","https://openalex.org/W4308408209","https://openalex.org/W4301783946","https://openalex.org/W4293226380","https://openalex.org/W3213976941","https://openalex.org/W3178607569","https://openalex.org/W2604192360","https://openalex.org/W2599472179"],"abstract_inverted_index":{"Deep":[0],"learning,":[1],"especially":[2],"convolutional":[3,93],"neural":[4],"networks,":[5],"has":[6],"triggered":[7],"accelerated":[8],"advancements":[9],"in":[10,83],"computer":[11,42],"vision,":[12],"bringing":[13],"changes":[14],"into":[15],"our":[16,112,123],"daily":[17],"practice.":[18],"Furthermore,":[19],"the":[20,56,61,92,106,115,127,130,134,149],"standardized":[21],"deep":[22,46],"learning":[23,47],"modules":[24],"(also":[25],"known":[26],"as":[27],"backbone":[28],"networks),":[29],"i.e.,":[30],"ResNet":[31],"and":[32,37,64,80,132],"EfficientNet,":[33],"have":[34,74],"enabled":[35],"efficient":[36],"rapid":[38],"development":[39],"of":[40,55,129,136,148],"new":[41],"vision":[43],"solutions.":[44],"Yet,":[45],"methods":[48],"still":[49],"suffer":[50],"from":[51,138],"several":[52],"drawbacks.":[53],"One":[54],"most":[57],"concerning":[58],"problems":[59],"is":[60,98],"high":[62],"memory":[63],"computational":[65],"cost,":[66],"such":[67],"that":[68],"dedicated":[69],"computing":[70],"units,":[71],"typically":[72],"GPUs,":[73],"to":[75,114,142],"be":[76],"used":[77],"for":[78,104],"training":[79],"development.":[81],"Therefore,":[82],"this":[84],"paper,":[85],"we":[86],"propose":[87],"a":[88],"quantifiable":[89],"evaluation":[90],"method,":[91],"kernel":[94],"redundancy":[95],"measure,":[96],"which":[97],"based":[99],"on":[100],"perceived":[101],"image":[102,118],"differences,":[103],"guiding":[105],"network":[107,131],"structure":[108],"simplification.":[109],"When":[110],"applying":[111],"method":[113,124],"chest":[116],"X-ray":[117],"classification":[119],"problem":[120],"with":[121],"ResNet,":[122],"can":[125],"maintain":[126],"performance":[128],"reduce":[133],"number":[135],"parameters":[137],"over":[139],"$23$":[140],"million":[141],"approximately":[143],"$128$":[144],"thousand":[145],"(reducing":[146],"$99.46\\%$":[147],"parameters).":[150]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4310629432","counts_by_year":[],"updated_date":"2024-12-09T21:39:51.056391","created_date":"2022-12-13"}