{"id":"https://openalex.org/W4205252812","doi":"https://doi.org/10.1109/bigdata52589.2021.9671980","title":"CUP: Cluster Pruning for Compressing Deep Neural Networks","display_name":"CUP: Cluster Pruning for Compressing Deep Neural Networks","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205252812","doi":"https://doi.org/10.1109/bigdata52589.2021.9671980"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671980","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671980","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5057757200","display_name":"Rahul Duggal","orcid":"https://orcid.org/0000-0001-7229-9548"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rahul Duggal","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100645991","display_name":"Cao Xiao","orcid":"https://orcid.org/0000-0002-3869-6942"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao Xiao","raw_affiliation_strings":["Amplitude"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amplitude","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016738770","display_name":"Richard Vuduc","orcid":"https://orcid.org/0000-0003-2178-138X"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard Vuduc","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020153026","display_name":"Duen Horng Chau","orcid":"https://orcid.org/0000-0001-9824-3323"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Duen Horng Chau","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084279065","display_name":"Jimeng Sun","orcid":"https://orcid.org/0000-0003-1512-6426"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jimeng Sun","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3367,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.88316832,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5102","last_page":"5106"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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":0.9991000294685364,"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/T12676","display_name":"Machine Learning and ELM","score":0.9941999912261963,"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/pruning","display_name":"Pruning","score":0.7723966836929321},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7603119611740112},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7377095222473145},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.6816046237945557},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.6238469481468201},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5555037260055542},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5411166548728943},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.5162894129753113},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4815381169319153},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45477601885795593},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4468671381473541},{"id":"https://openalex.org/keywords/sort","display_name":"sort","score":0.4350232779979706},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3981952667236328},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3817657232284546},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3420388698577881},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14438244700431824},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.07700759172439575}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7723966836929321},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7603119611740112},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7377095222473145},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.6816046237945557},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.6238469481468201},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5555037260055542},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5411166548728943},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.5162894129753113},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4815381169319153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45477601885795593},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4468671381473541},{"id":"https://openalex.org/C88548561","wikidata":"https://www.wikidata.org/wiki/Q347599","display_name":"sort","level":2,"score":0.4350232779979706},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3981952667236328},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3817657232284546},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3420388698577881},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14438244700431824},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.07700759172439575},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671980","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671980","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1821462560","https://openalex.org/W1902934009","https://openalex.org/W1996901117","https://openalex.org/W2119144962","https://openalex.org/W2194775991","https://openalex.org/W2495425901","https://openalex.org/W2767476316","https://openalex.org/W2795051673","https://openalex.org/W2808168148","https://openalex.org/W2886851211","https://openalex.org/W2896409484","https://openalex.org/W2924515500","https://openalex.org/W2928560789","https://openalex.org/W2952088488","https://openalex.org/W2962766718","https://openalex.org/W2962851801","https://openalex.org/W2962965870","https://openalex.org/W2963000224","https://openalex.org/W2963094099","https://openalex.org/W2963122961","https://openalex.org/W2963363373","https://openalex.org/W2963828549","https://openalex.org/W2964266063","https://openalex.org/W2965174861","https://openalex.org/W2984618279","https://openalex.org/W2993425728","https://openalex.org/W4293282014","https://openalex.org/W6637373629","https://openalex.org/W6638523607","https://openalex.org/W6639703010","https://openalex.org/W6677103964","https://openalex.org/W6677580257","https://openalex.org/W6723181079","https://openalex.org/W6725543821","https://openalex.org/W6726275242","https://openalex.org/W6732814185","https://openalex.org/W6745499552","https://openalex.org/W6746082227","https://openalex.org/W6748780714","https://openalex.org/W6755034786","https://openalex.org/W6755843862"],"related_works":["https://openalex.org/W4315697128","https://openalex.org/W4382323155","https://openalex.org/W3205506801","https://openalex.org/W2971502891","https://openalex.org/W4287067436","https://openalex.org/W4280599700","https://openalex.org/W3183570023","https://openalex.org/W3102845713","https://openalex.org/W2361805396","https://openalex.org/W3092292339"],"abstract_inverted_index":{"We":[0,48],"propose":[1],"CUP,":[2],"a":[3,44,73,82,89,99,115],"new":[4],"method":[5],"for":[6,36,152],"compressing":[7],"and":[8,21],"accelerating":[9],"deep":[10],"neural":[11],"networks.":[12],"At":[13],"its":[14],"core,":[15],"CUP":[16,30,54,69,92,112,153],"achieves":[17],"compression":[18],"by":[19,51],"clustering":[20,33],"pruning":[22,63],"similar":[23],"filters":[24],"in":[25,98,107,126,130,145],"each":[26],"layer.":[27],"For":[28],"clustering,":[29],"uses":[31],"hierarchical":[32],"which":[34],"allows":[35],"an":[37],"elegant":[38],"parameterization":[39],"of":[40,139],"model":[41,58,74,97],"capacity":[42,59],"through":[43,60,81,88],"single":[45,100],"hyper-parameter":[46],"t.":[47,86],"observe":[49],"that":[50],"increasing":[52],"t,":[53],"can":[55,70,93],"dynamically":[56],"reduce":[57],"non-uniform":[61],"layer-wise":[62],"leading":[64,103],"to":[65,75,104,114,147],"two":[66],"advantages.":[67],"First,":[68],"effectively":[71],"compress":[72],"within":[76],"the":[77,95,131],"desired":[78],"compute":[79],"budget":[80],"simple":[83,90],"line-search":[84],"on":[85,119,142],"Second,":[87],"extension,":[91],"obtain":[94],"pruned":[96],"training":[101,108,140],"pass":[102],"large":[105],"savings":[106],"time.":[109],"On":[110],"Imagenet,":[111],"leads":[113],"2.47\u00d7":[116],"FLOPS":[117],"reduction":[118],"Resnet-50":[120],"with":[121],"less":[122],"than":[123],"1%":[124],"drop":[125],"top-5":[127],"accuracy.":[128],"Notably,":[129],"retrain-free":[132],"setting,":[133],"CUP-RF":[134],"saves":[135],"over":[136],"10":[137],"hours":[138],"time":[141],"3":[143],"GPUs,":[144],"comparison":[146],"state-of-the-art":[148],"methods.":[149],"The":[150],"code":[151],"is":[154],"open":[155],"sourced1.":[156]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}