{"id":"https://openalex.org/W3010801994","doi":"https://doi.org/10.1109/icpr48806.2021.9412590","title":"MINT: Deep Network Compression via Mutual Information-based Neuron Trimming","display_name":"MINT: Deep Network Compression via Mutual Information-based Neuron Trimming","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3010801994","doi":"https://doi.org/10.1109/icpr48806.2021.9412590","mag":"3010801994"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412590","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2003.08472","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088604511","display_name":"Madan Ravi Ganesh","orcid":"https://orcid.org/0000-0003-1708-5509"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Madan Ravi Ganesh","raw_affiliation_strings":["EECS University of Michigan, Ann Arbor, Michigan","EECS University of Michigan,Ann Arbor,Michigan"],"affiliations":[{"raw_affiliation_string":"EECS University of Michigan, Ann Arbor, Michigan","institution_ids":["https://openalex.org/I27837315"]},{"raw_affiliation_string":"EECS University of Michigan,Ann Arbor,Michigan","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079301873","display_name":"Jason J. Corso","orcid":"https://orcid.org/0000-0001-6454-9594"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jason J. Corso","raw_affiliation_strings":["EECS University of Michigan, Ann Arbor, Michigan","EECS University of Michigan,Ann Arbor,Michigan"],"affiliations":[{"raw_affiliation_string":"EECS University of Michigan, Ann Arbor, Michigan","institution_ids":["https://openalex.org/I27837315"]},{"raw_affiliation_string":"EECS University of Michigan,Ann Arbor,Michigan","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081523775","display_name":"Salimeh Yasaei Sekeh","orcid":"https://orcid.org/0000-0002-0854-5422"},"institutions":[{"id":"https://openalex.org/I7947594","display_name":"University of Maine","ror":"https://ror.org/01adr0w49","country_code":"US","type":"education","lineage":["https://openalex.org/I2802397601","https://openalex.org/I7947594"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Salimeh Yasaei Sekeh","raw_affiliation_strings":["SCIS University of Maine, Orono, Maine","SCIS University of Maine,Orono,Maine"],"affiliations":[{"raw_affiliation_string":"SCIS University of Maine, Orono, Maine","institution_ids":["https://openalex.org/I7947594"]},{"raw_affiliation_string":"SCIS University of Maine,Orono,Maine","institution_ids":["https://openalex.org/I7947594"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088604511"],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.51262003,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"8251","last_page":"8258"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":1.0,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":1.0,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9988999962806702,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9986000061035156,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/trimming","display_name":"Trimming","score":0.8143680095672607},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.7347699403762817},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7306539416313171},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6783298254013062},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.6439074277877808},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.5129625201225281},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4955223798751831},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49252399802207947},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44816893339157104},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4278976619243622},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4169294834136963},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3831917643547058},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3407932221889496},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3357633650302887},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.24636423587799072},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.21200379729270935}],"concepts":[{"id":"https://openalex.org/C56951928","wikidata":"https://www.wikidata.org/wiki/Q3539213","display_name":"Trimming","level":2,"score":0.8143680095672607},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7347699403762817},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7306539416313171},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6783298254013062},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.6439074277877808},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.5129625201225281},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4955223798751831},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49252399802207947},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44816893339157104},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4278976619243622},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4169294834136963},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3831917643547058},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3407932221889496},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3357633650302887},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.24636423587799072},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.21200379729270935},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412590","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2003.08472","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.08472","pdf_url":"https://arxiv.org/pdf/2003.08472","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":null,"raw_type":"text"},{"id":"mag:3010801994","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2003.08472","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2003.08472","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2003.08472","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2003.08472","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.08472","pdf_url":"https://arxiv.org/pdf/2003.08472","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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G29995572","display_name":null,"funder_award_id":"60NANB17D191","funder_id":"https://openalex.org/F4320332178","funder_display_name":"National Institute of Standards and Technology"},{"id":"https://openalex.org/G4871409486","display_name":null,"funder_award_id":"1920908","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332178","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3010801994.pdf"},"referenced_works_count":70,"referenced_works":["https://openalex.org/W566555209","https://openalex.org/W1686810756","https://openalex.org/W1902041153","https://openalex.org/W1981050900","https://openalex.org/W1996901117","https://openalex.org/W2027390033","https://openalex.org/W2077584453","https://openalex.org/W2092939357","https://openalex.org/W2104636679","https://openalex.org/W2112796928","https://openalex.org/W2114766824","https://openalex.org/W2117539524","https://openalex.org/W2125389748","https://openalex.org/W2190746225","https://openalex.org/W2194775991","https://openalex.org/W2254249950","https://openalex.org/W2306911452","https://openalex.org/W2460937040","https://openalex.org/W2515385951","https://openalex.org/W2524428287","https://openalex.org/W2585429527","https://openalex.org/W2593829254","https://openalex.org/W2617669016","https://openalex.org/W2626967530","https://openalex.org/W2739879705","https://openalex.org/W2741485222","https://openalex.org/W2748818695","https://openalex.org/W2924515500","https://openalex.org/W2947963429","https://openalex.org/W2951349360","https://openalex.org/W2962679290","https://openalex.org/W2962851801","https://openalex.org/W2962858109","https://openalex.org/W2963000224","https://openalex.org/W2963114950","https://openalex.org/W2963145730","https://openalex.org/W2963207607","https://openalex.org/W2963273111","https://openalex.org/W2963363373","https://openalex.org/W2963382930","https://openalex.org/W2963550286","https://openalex.org/W2963674932","https://openalex.org/W2963864497","https://openalex.org/W2963981420","https://openalex.org/W2964200874","https://openalex.org/W2964233199","https://openalex.org/W2964299589","https://openalex.org/W2964313902","https://openalex.org/W2965658867","https://openalex.org/W2966994809","https://openalex.org/W2970971581","https://openalex.org/W2991040477","https://openalex.org/W2991849637","https://openalex.org/W3101158363","https://openalex.org/W3102411776","https://openalex.org/W3103145119","https://openalex.org/W3118608800","https://openalex.org/W6615861906","https://openalex.org/W6677103964","https://openalex.org/W6680940449","https://openalex.org/W6691692454","https://openalex.org/W6724998850","https://openalex.org/W6725543821","https://openalex.org/W6726275242","https://openalex.org/W6727208969","https://openalex.org/W6741047420","https://openalex.org/W6741783443","https://openalex.org/W6743087176","https://openalex.org/W6743914908","https://openalex.org/W6745676405"],"related_works":["https://openalex.org/W3160040323","https://openalex.org/W2995428361","https://openalex.org/W3036191539","https://openalex.org/W2771111398","https://openalex.org/W2978441820","https://openalex.org/W3202492111","https://openalex.org/W3044430233","https://openalex.org/W3117969921","https://openalex.org/W3093649253","https://openalex.org/W3158839075","https://openalex.org/W2999498703","https://openalex.org/W2786386501","https://openalex.org/W3184164445","https://openalex.org/W3121699489","https://openalex.org/W2785668315","https://openalex.org/W3155573568","https://openalex.org/W3023585672","https://openalex.org/W2891561769","https://openalex.org/W3119992913","https://openalex.org/W3102982004"],"abstract_inverted_index":{"Most":[0],"approaches":[1,65],"to":[2,34,186,194],"deep":[3,66],"neural":[4],"network":[5,163],"compression":[6,67],"via":[7,68],"pruning":[8,69,117,183],"either":[9,42],"directly":[10],"evaluate":[11],"a":[12,31,50,113,118,160,168,178],"filter's":[13],"importance":[14],"using":[15,97,112,166],"its":[16],"weights":[17],"or":[18,48],"optimize":[19],"an":[20],"alternative":[21],"objective":[22,54],"function":[23],"with":[24,144],"sparsity":[25,72],"constraints.":[26],"While":[27],"these":[28],"methods":[29,148],"offer":[30],"useful":[32],"way":[33],"approximate":[35],"contributions":[36],"from":[37],"similar":[38,107],"filters,":[39],"they":[40],"often":[41],"ignore":[43],"the":[44,75,78,91,104,126,129,195],"dependency":[45,79,94],"between":[46,80,110,181],"layers":[47,89,133],"solve":[49],"more":[51],"difficult":[52],"optimization":[53],"than":[55],"standard":[56,150],"cross-entropy.":[57],"Our":[58,138],"method,":[59],"Mutual":[60],"Information-based":[61],"Neuron":[62],"Trimming":[63],"(MINT),":[64],"by":[70],"enforcing":[71],"based":[73],"on":[74,149],"strength":[76],"of":[77,82,88,106,128,162,177],"filters":[81,111,124],"adjacent":[83],"layers,":[84],"across":[85,159],"every":[86],"pair":[87],"in":[90],"network.":[92,197],"The":[93],"is":[95,141],"calculated":[96],"conditional":[98],"geometric":[99],"mutual":[100],"information":[101,108,130],"which":[102,134],"evaluates":[103],"amount":[105],"exchanged":[109],"graph-based":[114],"criterion.":[115],"When":[116],"network,":[119],"we":[120,173],"ensure":[121],"that":[122],"retained":[123],"contribute":[125],"majority":[127],"towards":[131],"succeeding":[132],"ensures":[135],"high":[136],"performance.":[137],"novel":[139],"approach":[140],"highly":[142],"competitive":[143],"existing":[145],"state-of-the-art":[146],"compression-via-pruning":[147],"benchmarks":[151],"for":[152],"this":[153],"task:":[154],"MNIST,":[155],"CIFAR-10,":[156],"and":[157,189],"ILSVRC2012,":[158],"variety":[161],"architectures":[164],"despite":[165],"only":[167],"single":[169],"retraining":[170],"pass.":[171],"Also,":[172],"discuss":[174],"our":[175,182],"observations":[176],"common":[179],"denominator":[180],"methodology's":[184],"response":[185],"adversarial":[187],"attacks":[188],"calibration":[190],"statistics":[191],"when":[192],"compared":[193],"original":[196]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}