{"id":"https://openalex.org/W2978274700","doi":"https://doi.org/10.1109/ijcnn.2019.8851810","title":"Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning","display_name":"Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978274700","doi":"https://doi.org/10.1109/ijcnn.2019.8851810","mag":"2978274700"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5033661654","display_name":"Chaohui Yu","orcid":"https://orcid.org/0000-0002-7852-4491"},"institutions":[{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chaohui Yu","raw_affiliation_strings":["Beijing Key Lab. of Mobile Computing and Pervasive Device, Inst. of Computing Technology, CAS, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Mobile Computing and Pervasive Device, Inst. of Computing Technology, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210090176"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100700956","display_name":"Jindong Wang","orcid":"https://orcid.org/0000-0002-4833-0880"},"institutions":[{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jindong Wang","raw_affiliation_strings":["Beijing Key Lab. of Mobile Computing and Pervasive Device, Inst. of Computing Technology, CAS, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Mobile Computing and Pervasive Device, Inst. of Computing Technology, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210090176"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008558592","display_name":"Yiqiang Chen","orcid":"https://orcid.org/0000-0002-8407-0780"},"institutions":[{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiqiang Chen","raw_affiliation_strings":["Beijing Key Lab. of Mobile Computing and Pervasive Device, Inst. of Computing Technology, CAS, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Mobile Computing and Pervasive Device, Inst. of Computing Technology, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210090176"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081448725","display_name":"Zijing Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zijing Wu","raw_affiliation_strings":["Columbia University, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5033661654"],"corresponding_institution_ids":["https://openalex.org/I4210090176"],"apc_list":null,"apc_paid":null,"fwci":1.7345,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.88875662,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998999834060669,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998999834060669,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9897000193595886,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9783999919891357,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8535590171813965},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7544382810592651},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6852067112922668},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6783359050750732},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6378167867660522},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.6236670017242432},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6006678938865662},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5862913131713867},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.44786447286605835},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.42594027519226074},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42055544257164},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.13651427626609802},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.12229391932487488}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8535590171813965},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7544382810592651},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6852067112922668},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6783359050750732},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6378167867660522},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.6236670017242432},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6006678938865662},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5862913131713867},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.44786447286605835},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.42594027519226074},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42055544257164},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.13651427626609802},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.12229391932487488},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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":73,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W1686810756","https://openalex.org/W1722318740","https://openalex.org/W1731081199","https://openalex.org/W1836465849","https://openalex.org/W2096943734","https://openalex.org/W2104068492","https://openalex.org/W2107298017","https://openalex.org/W2115403315","https://openalex.org/W2119144962","https://openalex.org/W2149933564","https://openalex.org/W2155541015","https://openalex.org/W2158815628","https://openalex.org/W2159291411","https://openalex.org/W2161381512","https://openalex.org/W2163605009","https://openalex.org/W2163922914","https://openalex.org/W2165698076","https://openalex.org/W2167215970","https://openalex.org/W2194775991","https://openalex.org/W2300242332","https://openalex.org/W2329664094","https://openalex.org/W2363300041","https://openalex.org/W2520436654","https://openalex.org/W2531409750","https://openalex.org/W2593245696","https://openalex.org/W2593768305","https://openalex.org/W2627183927","https://openalex.org/W2707890836","https://openalex.org/W2735189290","https://openalex.org/W2752037867","https://openalex.org/W2805933112","https://openalex.org/W2884771968","https://openalex.org/W2890336707","https://openalex.org/W2899771611","https://openalex.org/W2950248853","https://openalex.org/W2950635901","https://openalex.org/W2950784788","https://openalex.org/W2962808524","https://openalex.org/W2962835968","https://openalex.org/W2963214104","https://openalex.org/W2963275094","https://openalex.org/W2963287528","https://openalex.org/W2963363373","https://openalex.org/W2963674932","https://openalex.org/W2963693396","https://openalex.org/W2964228333","https://openalex.org/W2964233199","https://openalex.org/W2964299589","https://openalex.org/W3098053970","https://openalex.org/W4294375521","https://openalex.org/W4299518610","https://openalex.org/W6600949241","https://openalex.org/W6637373629","https://openalex.org/W6637618735","https://openalex.org/W6638667902","https://openalex.org/W6676215431","https://openalex.org/W6677580257","https://openalex.org/W6682132143","https://openalex.org/W6682778277","https://openalex.org/W6683401423","https://openalex.org/W6683633756","https://openalex.org/W6684191040","https://openalex.org/W6684563725","https://openalex.org/W6685415078","https://openalex.org/W6698200048","https://openalex.org/W6734062232","https://openalex.org/W6739917289","https://openalex.org/W6743912273","https://openalex.org/W6746282794","https://openalex.org/W6751876697","https://openalex.org/W6754528357","https://openalex.org/W6756040250"],"related_works":["https://openalex.org/W4315697128","https://openalex.org/W3102845713","https://openalex.org/W2971502891","https://openalex.org/W3183901164","https://openalex.org/W2951211570","https://openalex.org/W3135818718","https://openalex.org/W4290188444","https://openalex.org/W3003905048","https://openalex.org/W2253429366","https://openalex.org/W3127975138"],"abstract_inverted_index":{"Deep":[0],"unsupervised":[1],"domain":[2],"adaptation":[3],"(UDA)":[4],"has":[5],"recently":[6],"received":[7],"increasing":[8],"attention":[9],"from":[10],"researchers.":[11],"However,":[12],"existing":[13],"methods":[14,152],"are":[15],"computationally":[16],"intensive":[17],"due":[18],"to":[19],"the":[20,66,82,89,99,103,110,156,187],"computation":[21],"cost":[22],"of":[23,64,91,105,186],"Convolutional":[24],"Neural":[25],"Networks":[26],"(CNN)":[27],"adopted":[28],"by":[29,70,80],"most":[30],"work.":[31],"To":[32,102,159],"date,":[33],"there":[34],"is":[35,62,109,121],"no":[36],"effective":[37],"network":[38],"compression":[39],"method":[40],"for":[41,57,202],"accelerating":[42,58,116,206],"these":[43],"models.":[44,60,119,209],"In":[45],"this":[46],"paper,":[47],"we":[48,165,179],"propose":[49],"a":[50,199],"unified":[51],"Transfer":[52],"Channel":[53],"Pruning":[54],"(TCP)":[55],"approach":[56,112],"UDA":[59,68,118],"TCP":[61,108,120,142,196],"capable":[63],"compressing":[65],"deep":[67,117],"model":[69],"pruning":[71,171,190],"less":[72],"important":[73],"channels":[74],"while":[75,153],"simultaneously":[76],"learning":[77,208],"transferable":[78],"features":[79],"reducing":[81,155],"cross-domain":[83],"distribution":[84],"divergence.":[85],"Therefore,":[86],"it":[87],"reduces":[88],"impact":[90],"negative":[92],"transfer":[93,207],"and":[94,128,136],"maintains":[95],"competitive":[96],"performance":[97],"on":[98,123,184,205],"target":[100],"task.":[101],"best":[104],"our":[106],"knowledge,":[107],"first":[111],"that":[113,141,195],"aims":[114],"at":[115],"validated":[122],"two":[124,131],"benchmark":[125],"datasets":[126],"Office-31":[127],"ImageCLEF-DA":[129],"with":[130],"common":[132],"backbone":[133],"networks":[134],"VGG16":[135],"ResNet50.":[137],"Experimental":[138],"results":[139],"demonstrate":[140],"achieves":[143],"comparable":[144],"or":[145],"better":[146],"classification":[147],"accuracy":[148,169,183],"than":[149],"other":[150],"comparison":[151],"significantly":[154],"computational":[157],"cost.":[158],"be":[160],"more":[161],"specific,":[162],"in":[163,177],"VGG16,":[164],"get":[166,181],"even":[167],"higher":[168,182],"after":[170,189],"26%":[172],"floating":[173],"point":[174],"operations":[175],"(FLOPs);":[176],"ResNet50,":[178],"also":[180],"half":[185],"tasks":[188],"12%":[191],"FLOPs.":[192],"We":[193],"hope":[194],"will":[197],"open":[198],"new":[200],"door":[201],"future":[203],"research":[204]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}