{"id":"https://openalex.org/W3009616221","doi":"https://doi.org/10.1109/wacv45572.2020.9093318","title":"Is Pruning Compression?: Investigating Pruning Via Network Layer Similarity","display_name":"Is Pruning Compression?: Investigating Pruning Via Network Layer Similarity","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3009616221","doi":"https://doi.org/10.1109/wacv45572.2020.9093318","mag":"3009616221"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093318","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002287514","display_name":"Cody Blakeney","orcid":"https://orcid.org/0000-0002-1412-2813"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cody Blakeney","raw_affiliation_strings":["Texas State University"],"affiliations":[{"raw_affiliation_string":"Texas State University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100395059","display_name":"Yan Yan","orcid":"https://orcid.org/0000-0002-3674-7160"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan Yan","raw_affiliation_strings":["Texas State University"],"affiliations":[{"raw_affiliation_string":"Texas State University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008451482","display_name":"Ziliang Zong","orcid":"https://orcid.org/0000-0003-2693-7419"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ziliang Zong","raw_affiliation_strings":["Texas State University"],"affiliations":[{"raw_affiliation_string":"Texas State University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002287514"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9279,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.79750055,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"903","last_page":"911"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9990000128746033,"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/T10320","display_name":"Neural Networks and Applications","score":0.9990000128746033,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.892035722732544},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.8257328271865845},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7369171380996704},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6301693916320801},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5395277142524719},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42360588908195496},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.410507470369339},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39825719594955444},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39709538221359253},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.25521570444107056}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.892035722732544},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.8257328271865845},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7369171380996704},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6301693916320801},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5395277142524719},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42360588908195496},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.410507470369339},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39825719594955444},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39709538221359253},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25521570444107056},{"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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093318","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.9100000262260437,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W566555209","https://openalex.org/W1686810756","https://openalex.org/W2097117768","https://openalex.org/W2114766824","https://openalex.org/W2121775913","https://openalex.org/W2123469553","https://openalex.org/W2163605009","https://openalex.org/W2172166488","https://openalex.org/W2194775991","https://openalex.org/W2276892413","https://openalex.org/W2520760693","https://openalex.org/W2544912866","https://openalex.org/W2608554408","https://openalex.org/W2766839578","https://openalex.org/W2767204723","https://openalex.org/W2783538964","https://openalex.org/W2805003733","https://openalex.org/W2896409484","https://openalex.org/W2952432176","https://openalex.org/W2962685937","https://openalex.org/W2963225922","https://openalex.org/W2963643655","https://openalex.org/W2963674932","https://openalex.org/W2963813662","https://openalex.org/W4285719527","https://openalex.org/W4297689207","https://openalex.org/W4297730150","https://openalex.org/W4302088125","https://openalex.org/W6637373629","https://openalex.org/W6638632666","https://openalex.org/W6677103964","https://openalex.org/W6684191040","https://openalex.org/W6685405536","https://openalex.org/W6685891324","https://openalex.org/W6687483927","https://openalex.org/W6694806693","https://openalex.org/W6697462184","https://openalex.org/W6729471031","https://openalex.org/W6733074585","https://openalex.org/W6736780897","https://openalex.org/W6745682157","https://openalex.org/W6751979845","https://openalex.org/W6755843862"],"related_works":["https://openalex.org/W3204184292","https://openalex.org/W3176564347","https://openalex.org/W1985458517","https://openalex.org/W2355833770","https://openalex.org/W3031039437","https://openalex.org/W183202219","https://openalex.org/W3095877357","https://openalex.org/W2951598131","https://openalex.org/W4221159459","https://openalex.org/W3138851421"],"abstract_inverted_index":{"Unstructured":[0],"neural":[1,22,44,67,139],"network":[2,140],"pruning":[3,34,55,62,93,164,171],"is":[4,70],"an":[5],"effective":[6],"technique":[7],"that":[8],"can":[9,156],"significantly":[10],"reduce":[11],"theoretical":[12],"model":[13],"size,":[14],"computation":[15],"demand":[16],"and":[17,73,100,119,125,129],"energy":[18],"consumption":[19],"of":[20,30,75,123],"large":[21],"networks":[23,45],"without":[24],"compromising":[25],"accuracy.":[26,151],"However,":[27],"a":[28,56],"number":[29],"fundamental":[31],"questions":[32,88],"about":[33],"are":[35,173],"not":[36,174],"answered":[37],"yet.":[38],"For":[39],"example,":[40],"do":[41],"the":[42,47,51,71,76,109],"pruned":[43,124],"contain":[46],"same":[48],"representations":[49,122,146],"as":[50],"original":[52,126],"network?":[53],"Is":[54],"compression":[57],"or":[58,177],"evolution":[59],"process?":[60],"Does":[61],"only":[63],"work":[64],"on":[65],"trained":[66],"networks?":[68],"What":[69],"role":[72],"value":[74],"uncovered":[77],"sparsity":[78],"structure?":[79],"In":[80],"this":[81],"paper,":[82],"we":[83],"strive":[84],"to":[85,117,143,162],"answer":[86],"these":[87],"by":[89,170],"analyzing":[90],"three":[91],"unstructured":[92],"methods":[94],"(magnitude":[95],"based":[96],"pruning,":[97],"post-pruning":[98],"re-initialization,":[99],"random":[101],"sparse":[102,154],"initialization).":[103],"We":[104,132],"conduct":[105],"extensive":[106],"experiments":[107],"using":[108],"Singular":[110],"Vector":[111],"Canonical":[112],"Correlation":[113],"Analysis":[114],"(SVCCA)":[115],"tool":[116],"study":[118],"contrast":[120],"layer":[121],"ResNet,":[127],"VGG,":[128],"ConvNet":[130],"models.":[131],"have":[133],"several":[134],"interesting":[135],"observations:":[136],"1)":[137],"Pruned":[138],"models":[141,155,172],"evolve":[142],"substantially":[144],"different":[145],"while":[147],"still":[148],"maintaining":[149],"similar":[150],"2)":[152],"Initialized":[153],"achieve":[157],"reasonably":[158],"good":[159],"accuracy":[160],"compared":[161],"well-engineered":[163],"methods.":[165],"3)":[166],"Sparsity":[167],"structures":[168],"discovered":[169],"inherently":[175],"important":[176],"useful.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}