{"id":"https://openalex.org/W3034755948","doi":"https://doi.org/10.1109/icme46284.2020.9102797","title":"Lightweight Compression Of Neural Network Feature Tensors For Collaborative Intelligence","display_name":"Lightweight Compression Of Neural Network Feature Tensors For Collaborative Intelligence","publication_year":2020,"publication_date":"2020-06-09","ids":{"openalex":"https://openalex.org/W3034755948","doi":"https://doi.org/10.1109/icme46284.2020.9102797","mag":"3034755948"},"language":"en","primary_location":{"id":"doi:10.1109/icme46284.2020.9102797","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme46284.2020.9102797","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2105.06002","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Robert A. Cohen","orcid":null},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Robert A. Cohen","raw_affiliation_strings":["School of Engineering Science, Simon Fraser University, BC, Burnaby, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering Science, Simon Fraser University, BC, Burnaby, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hyomin Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Hyomin Choi","raw_affiliation_strings":["School of Engineering Science, Simon Fraser University, BC, Burnaby, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering Science, Simon Fraser University, BC, Burnaby, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ivan V. Bajic","orcid":null},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ivan V. Bajic","raw_affiliation_strings":["School of Engineering Science, Simon Fraser University, BC, Burnaby, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering Science, Simon Fraser University, BC, Burnaby, Canada","institution_ids":["https://openalex.org/I18014758"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I18014758"],"apc_list":null,"apc_paid":null,"fwci":2.4773,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.92489912,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9990000128746033,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9990000128746033,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9986000061035156,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6614999771118164},{"id":"https://openalex.org/keywords/codec","display_name":"Codec","score":0.6593999862670898},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5302000045776367},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.4636000096797943},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.4489000141620636},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.44209998846054077},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4404999911785126},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4275999963283539},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.42669999599456787}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.802299976348877},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6614999771118164},{"id":"https://openalex.org/C161765866","wikidata":"https://www.wikidata.org/wiki/Q184748","display_name":"Codec","level":2,"score":0.6593999862670898},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5386000275611877},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5302000045776367},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.4636000096797943},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.4489000141620636},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.44209998846054077},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4404999911785126},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4275999963283539},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.42669999599456787},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.4050000011920929},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3537999987602234},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3393000066280365},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C84211073","wikidata":"https://www.wikidata.org/wiki/Q117879","display_name":"Floating point","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.28519999980926514},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.272599995136261},{"id":"https://openalex.org/C94835093","wikidata":"https://www.wikidata.org/wiki/Q3113333","display_name":"Data compression ratio","level":5,"score":0.26660001277923584},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.25859999656677246},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C39613435","wikidata":"https://www.wikidata.org/wiki/Q846677","display_name":"Remainder","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25040000677108765}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icme46284.2020.9102797","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme46284.2020.9102797","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2105.06002","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.06002","pdf_url":"https://arxiv.org/pdf/2105.06002","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2105.06002","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.06002","pdf_url":"https://arxiv.org/pdf/2105.06002","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2033140753","https://openalex.org/W2117539524","https://openalex.org/W2129768577","https://openalex.org/W2131578006","https://openalex.org/W2169161136","https://openalex.org/W2194775991","https://openalex.org/W2249152912","https://openalex.org/W2605258629","https://openalex.org/W2804309962","https://openalex.org/W2918274546","https://openalex.org/W2960833983","https://openalex.org/W2971714613","https://openalex.org/W6617368339","https://openalex.org/W6639102338","https://openalex.org/W6693397755","https://openalex.org/W6698200048","https://openalex.org/W6743755670","https://openalex.org/W6748172317","https://openalex.org/W6750227808","https://openalex.org/W6750608254","https://openalex.org/W6751528251","https://openalex.org/W6753069482","https://openalex.org/W6754259177","https://openalex.org/W6758625146","https://openalex.org/W6759144272","https://openalex.org/W6774071849","https://openalex.org/W6779885597"],"related_works":[],"abstract_inverted_index":{"In":[0],"collaborative":[1],"intelligence":[2],"applications,":[3],"part":[4],"of":[5,29,59,148],"a":[6,14,20,48,60,66,81,160],"deep":[7],"neural":[8,165],"network":[9],"(DNN)":[10],"is":[11,32],"deployed":[12],"on":[13],"relatively":[15],"low-complexity":[16],"device":[17],"such":[18,40],"as":[19,41],"mobile":[21],"phone":[22],"or":[23],"edge":[24,71],"device,":[25],"and":[26,73,96,146],"the":[27,30,43,57,104,117,132],"remainder":[28],"DNN":[31,62],"processed":[33],"where":[34],"more":[35],"computing":[36],"resources":[37],"are":[38],"available,":[39],"in":[42,119,163],"cloud.":[44],"This":[45],"paper":[46],"presents":[47],"novel":[49],"lightweight":[50,133,150],"compression":[51,151],"technique":[52,152],"designed":[53],"specifically":[54],"to":[55,93,102,110,112,121,127,142],"code":[56],"activations":[58,108,162],"split":[61,164],"layer,":[63],"while":[64,115],"having":[65],"low":[67],"complexity":[68],"suitable":[69],"for":[70,88,158,167],"devices":[72],"not":[74],"requiring":[75],"any":[76],"retraining.":[77],"We":[78],"also":[79],"present":[80],"modified":[82],"entropy-constrained":[83],"quantizer":[84],"design":[85],"algorithm":[86],"optimized":[87],"clipped":[89],"activations.":[90],"When":[91,125],"applied":[92],"popular":[94],"object-detection":[95],"classification":[97],"DNNs,":[98],"we":[99,129],"were":[100],"able":[101],"compress":[103],"32-bit":[105],"floating":[106],"point":[107],"down":[109],"0.6":[111],"0.8":[113],"bits,":[114],"keeping":[116],"loss":[118],"accuracy":[120],"less":[122],"than":[123],"1%.":[124],"compared":[126],"HEVC,":[128],"found":[130],"that":[131],"codec":[134],"consistently":[135],"provided":[136],"better":[137],"inference":[138],"accuracy,":[139],"by":[140],"up":[141],"1.3%.":[143],"The":[144],"performance":[145],"simplicity":[147],"this":[149],"makes":[153],"it":[154],"an":[155],"attractive":[156],"option":[157],"coding":[159],"layer's":[161],"networks":[166],"edge/cloud":[168],"applications.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":9}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2020-06-19T00:00:00"}