{"id":"https://openalex.org/W2979872554","doi":"https://doi.org/10.1145/3360512","title":"Distill-Net","display_name":"Distill-Net","publication_year":2019,"publication_date":"2019-09-30","ids":{"openalex":"https://openalex.org/W2979872554","doi":"https://doi.org/10.1145/3360512","mag":"2979872554"},"language":"en","primary_location":{"id":"doi:10.1145/3360512","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3360512","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3360512","source":{"id":"https://openalex.org/S136160450","display_name":"ACM Transactions on Embedded Computing Systems","issn_l":"1539-9087","issn":["1539-9087","1558-3465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Embedded Computing Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3360512","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101525467","display_name":"Mohammad Motamedi","orcid":"https://orcid.org/0000-0003-0120-8738"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammad Motamedi","raw_affiliation_strings":["University of California, Davis, CA, USA"],"raw_orcid":"https://orcid.org/0000-0003-0120-8738","affiliations":[{"raw_affiliation_string":"University of California, Davis, CA, USA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001421790","display_name":"Felix Portillo","orcid":null},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Felix A. Portillo","raw_affiliation_strings":["University of California, Davis, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Davis, CA, USA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008060394","display_name":"Daniel Fong","orcid":"https://orcid.org/0000-0002-7171-1171"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Fong","raw_affiliation_strings":["University of California, Davis, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-7171-1171","affiliations":[{"raw_affiliation_string":"University of California, Davis, CA, USA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031842294","display_name":"Soheil Ghiasi","orcid":"https://orcid.org/0000-0002-1036-791X"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Soheil Ghiasi","raw_affiliation_strings":["University of California, Davis, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Davis, CA, USA","institution_ids":["https://openalex.org/I84218800"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I84218800"],"apc_list":null,"apc_paid":null,"fwci":0.5079,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.69904579,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"18","issue":"5","first_page":"1","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9934999942779541,"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/computer-science","display_name":"Computer science","score":0.9170182943344116},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6739002466201782},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6275224685668945},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5848932266235352},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5802876949310303},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.526559591293335},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.51510089635849},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4650583863258362}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9170182943344116},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6739002466201782},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6275224685668945},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5848932266235352},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5802876949310303},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.526559591293335},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.51510089635849},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4650583863258362},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3360512","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3360512","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3360512","source":{"id":"https://openalex.org/S136160450","display_name":"ACM Transactions on Embedded Computing Systems","issn_l":"1539-9087","issn":["1539-9087","1558-3465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Embedded Computing Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3360512","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3360512","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3360512","source":{"id":"https://openalex.org/S136160450","display_name":"ACM Transactions on Embedded Computing Systems","issn_l":"1539-9087","issn":["1539-9087","1558-3465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Embedded Computing Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2979872554.pdf","grobid_xml":"https://content.openalex.org/works/W2979872554.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1972012147","https://openalex.org/W2094756095","https://openalex.org/W2097117768","https://openalex.org/W2105103777","https://openalex.org/W2117539524","https://openalex.org/W2155893237","https://openalex.org/W2163605009","https://openalex.org/W2272300165","https://openalex.org/W2276486856","https://openalex.org/W2279098554","https://openalex.org/W2289252105","https://openalex.org/W2337344472","https://openalex.org/W2431931973","https://openalex.org/W2525951180","https://openalex.org/W2598097916","https://openalex.org/W2606722458","https://openalex.org/W2750784772","https://openalex.org/W2752037867","https://openalex.org/W2758475033","https://openalex.org/W2962697884","https://openalex.org/W2963547613","https://openalex.org/W2964299589","https://openalex.org/W3001665736","https://openalex.org/W3104331387","https://openalex.org/W4236965008"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3181746755","https://openalex.org/W2521062615","https://openalex.org/W3016958897","https://openalex.org/W4224009465","https://openalex.org/W4283379348","https://openalex.org/W4287776258","https://openalex.org/W3027997911","https://openalex.org/W3021430260"],"abstract_inverted_index":{"Many":[0],"Internet-of-Things":[1],"(IoT)":[2],"applications":[3,159],"demand":[4],"fast":[5,64],"and":[6,33,87,154],"accurate":[7],"understanding":[8],"of":[9,40,105,120,157],"a":[10,62,85,92,142],"few":[11,118],"key":[12],"events":[13],"in":[14,54],"their":[15],"surrounding":[16],"environment.":[17],"Deep":[18],"Convolutional":[19],"Neural":[20],"Networks":[21],"(CNNs)":[22],"have":[23],"emerged":[24],"as":[25],"an":[26,109],"effective":[27],"approach":[28,140],"to":[29,99,122],"understand":[30],"speech,":[31],"images,":[32],"similar":[34],"high-dimensional":[35],"data":[36,107],"types.":[37],"Algorithmic":[38],"performance":[39],"modern":[41,94],"CNNs,":[42],"however,":[43],"fundamentally":[44],"relies":[45],"on":[46,69,130],"learning":[47],"class-agnostic":[48],"hierarchical":[49],"features":[50],"that":[51,96,113,138],"only":[52,115],"exist":[53],"comprehensive":[55],"training":[56],"datasets":[57,71],"with":[58,132],"many":[59,102],"classes.":[60],"As":[61],"result,":[63],"inference":[65,150],"using":[66],"CNNs":[67],"trained":[68,98],"such":[70],"is":[72,97],"prohibitive":[73],"for":[74,90],"most":[75],"resource-constrained":[76],"IoT":[77],"platforms.":[78],"To":[79],"bridge":[80],"this":[81],"gap,":[82],"we":[83],"present":[84],"principled":[86],"practical":[88],"methodology":[89],"distilling":[91],"complex":[93],"CNN":[95],"effectively":[100],"recognize":[101],"different":[103],"classes":[104,119],"input":[106],"into":[108],"application-dependent":[110],"essential":[111],"core":[112],"not":[114],"recognizes":[116],"the":[117,123],"interest":[121],"application":[124],"accurately":[125],"but":[126],"also":[127],"runs":[128],"efficiently":[129],"platforms":[131],"limited":[133],"resources.":[134],"Experimental":[135],"results":[136],"confirm":[137],"our":[139],"strikes":[141],"favorable":[143],"balance":[144],"between":[145],"classification":[146],"accuracy":[147],"(application":[148],"constraint),":[149,153],"efficiency":[151],"(platform":[152],"productive":[155],"development":[156],"new":[158],"(business":[160],"constraint).":[161]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2019-10-18T00:00:00"}
