{"id":"https://openalex.org/W2949635765","doi":null,"title":"A Taxonomy of Channel Pruning Signals in CNNs","display_name":"A Taxonomy of Channel Pruning Signals in CNNs","publication_year":2019,"publication_date":"2019-06-11","ids":{"openalex":"https://openalex.org/W2949635765","mag":"2949635765"},"language":"en","primary_location":{"id":"mag:2949635765","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1906.04675","pdf_url":null,"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,"listed_in":[],"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},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/1906.04675","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088959714","display_name":"Kaveena Persand","orcid":"https://orcid.org/0000-0002-5019-112X","observed_orcids":["https://orcid.org/0000-0002-5019-112X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaveena Persand","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Andrew Anderson","orcid":"https://orcid.org/0000-0002-4357-4739","observed_orcids":["https://orcid.org/0000-0002-4357-4739"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrew Anderson","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4357-4739","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5003800161","display_name":"David Gregg","orcid":"https://orcid.org/0000-0003-3782-4612","observed_orcids":["https://orcid.org/0000-0003-3782-4612"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"David Gregg","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-3782-4612","affiliations":[]}],"institutions":[],"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":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9994999766349792,"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/T12535","display_name":"Machine Learning and Data Classification","score":9.999999747378752e-05,"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":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/neural-network-pruning","display_name":"neural network pruning","score":0.781000018119812},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.847000002861023},{"id":"https://openalex.org/keywords/parameter-pruning","display_name":"parameter pruning","score":0.44999998807907104},{"id":"https://openalex.org/keywords/resource-constrained-systems","display_name":"resource-constrained systems","score":0.843999981880188},{"id":"https://openalex.org/keywords/signal-components","display_name":"signal components","score":0.39500001072883606}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8395833969116211},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7705909013748169},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6374126076698303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5440205931663513},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.46898940205574036},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41830796003341675},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41291457414627075},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.12050610780715942},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2949635765","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1906.04675","pdf_url":null,"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,"listed_in":[],"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}],"best_oa_location":{"id":"mag:2949635765","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1906.04675","pdf_url":null,"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,"listed_in":[],"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},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W566555209","https://openalex.org/W2097533491","https://openalex.org/W2108214478","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2114766824","https://openalex.org/W2123469553","https://openalex.org/W2125389748","https://openalex.org/W2134273960","https://openalex.org/W2141155619","https://openalex.org/W2145085734","https://openalex.org/W2145287260","https://openalex.org/W2155893237","https://openalex.org/W2156913264","https://openalex.org/W2194775991","https://openalex.org/W2276892413","https://openalex.org/W2495425901","https://openalex.org/W2513419314","https://openalex.org/W2549252813","https://openalex.org/W2589646680","https://openalex.org/W2592875630","https://openalex.org/W2618305643","https://openalex.org/W2736953746","https://openalex.org/W2757143157","https://openalex.org/W2886851211","https://openalex.org/W2898360562","https://openalex.org/W2899994076","https://openalex.org/W2910280493","https://openalex.org/W2914554687","https://openalex.org/W2921672511","https://openalex.org/W2928560789","https://openalex.org/W2945908221","https://openalex.org/W2947565354","https://openalex.org/W2955419283","https://openalex.org/W2963145730","https://openalex.org/W2963363373","https://openalex.org/W2963674932","https://openalex.org/W2963813662","https://openalex.org/W2970632972","https://openalex.org/W2971275988","https://openalex.org/W2994881943","https://openalex.org/W3084862391","https://openalex.org/W3118608800","https://openalex.org/W3136450870","https://openalex.org/W4394670654"],"related_works":["https://openalex.org/W2990619046","https://openalex.org/W4402226173","https://openalex.org/W3127765145","https://openalex.org/W3128642798","https://openalex.org/W3021594706","https://openalex.org/W3016092905","https://openalex.org/W2900011986","https://openalex.org/W3022581078","https://openalex.org/W2972024893","https://openalex.org/W2889287351"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"networks":[2],"(CNNs)":[3],"are":[4,21,54],"widely":[5],"used":[6],"for":[7],"classification":[8],"problems.":[9],"However,":[10,48],"they":[11],"often":[12],"require":[13],"large":[14],"amounts":[15],"of":[16,42,75,95,102],"computation":[17],"and":[18,88],"memory":[19],"which":[20],"not":[22],"readily":[23],"available":[24],"in":[25,45,51],"resource":[26],"constrained":[27],"systems.":[28],"Pruning":[29],"unimportant":[30],"parameters":[31],"from":[32,92],"CNNs":[33],"to":[34,57],"reduce":[35],"these":[36],"requirements":[37],"has":[38],"been":[39],"a":[40,64],"subject":[41],"intensive":[43],"research":[44],"recent":[46],"years.":[47],"novel":[49],"approaches":[50],"pruning":[52,68,83,111],"signals":[53,69,84,90,106],"sometimes":[55],"difficult":[56],"compare":[58],"against":[59],"each":[60],"other.":[61],"We":[62,78,98],"propose":[63],"taxonomy":[65],"that":[66,100],"classifies":[67],"based":[70],"on":[71],"four":[72],"mostly-orthogonal":[73],"components":[74,94],"the":[76,93,108],"signal.":[77],"also":[79],"empirically":[80],"evaluate":[81],"396":[82],"including":[85],"existing":[86,96,110],"ones,":[87],"new":[89],"constructed":[91,105],"signals.":[97,112],"find":[99],"some":[101],"our":[103],"newly":[104],"outperform":[107],"best":[109]},"counts_by_year":[],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2019-06-27T00:00:00"}
