{"id":"https://openalex.org/W4402351787","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650609","title":"Combining Pruning Strategies to Generate Efficient Inpainting Networks","display_name":"Combining Pruning Strategies to Generate Efficient Inpainting Networks","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351787","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650609"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650609","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650609","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 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/A5054799011","display_name":"Walber M. Rodrigues","orcid":"https://orcid.org/0000-0002-8809-6304"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Walber M. Rodrigues","raw_affiliation_strings":["Centro de Inform&#x00E1;tica - Universidade Federal de Pernambuco,Recife,Pernambuco,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centro de Inform&#x00E1;tica - Universidade Federal de Pernambuco,Recife,Pernambuco,Brazil","institution_ids":["https://openalex.org/I25112270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082717229","display_name":"Felipe N. Walmsley","orcid":"https://orcid.org/0000-0003-1410-1059"},"institutions":[{"id":"https://openalex.org/I4210161640","display_name":"Samsung (Brazil)","ror":"https://ror.org/052a20h63","country_code":"BR","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210161640"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Felipe N. Walmsley","raw_affiliation_strings":["CIn/Samsung Research and Development Lab,Recife,Pernambuco,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CIn/Samsung Research and Development Lab,Recife,Pernambuco,Brazil","institution_ids":["https://openalex.org/I4210161640"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041696034","display_name":"Jonysberg P. Quintino","orcid":"https://orcid.org/0000-0003-4667-2243"},"institutions":[{"id":"https://openalex.org/I4210161640","display_name":"Samsung (Brazil)","ror":"https://ror.org/052a20h63","country_code":"BR","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210161640"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jonysberg P. Quintino","raw_affiliation_strings":["CIn/Samsung Research and Development Lab,Recife,Pernambuco,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CIn/Samsung Research and Development Lab,Recife,Pernambuco,Brazil","institution_ids":["https://openalex.org/I4210161640"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025904983","display_name":"Helder Pinho","orcid":"https://orcid.org/0000-0002-9547-0640"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Helder S. Pinho","raw_affiliation_strings":["SiDi,S&#x00E3;o Paulo,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SiDi,S&#x00E3;o Paulo,Brazil","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084140678","display_name":"George D. C. Cavalcanti","orcid":"https://orcid.org/0000-0001-7714-2283"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"George D.C. Cavalcanti","raw_affiliation_strings":["Centro de Inform&#x00E1;tica - Universidade Federal de Pernambuco,Recife,Pernambuco,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centro de Inform&#x00E1;tica - Universidade Federal de Pernambuco,Recife,Pernambuco,Brazil","institution_ids":["https://openalex.org/I25112270"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5054799011"],"corresponding_institution_ids":["https://openalex.org/I25112270"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13643683,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"30","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9952999949455261,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9952999949455261,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9641000032424927,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9595999717712402,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/inpainting","display_name":"Inpainting","score":0.9083068370819092},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7757139801979065},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6978430151939392},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6054466366767883},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3331329822540283},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16987377405166626}],"concepts":[{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.9083068370819092},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7757139801979065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6978430151939392},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6054466366767883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3331329822540283},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16987377405166626},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650609","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650609","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2194775991","https://openalex.org/W2331128040","https://openalex.org/W2507296351","https://openalex.org/W2531409750","https://openalex.org/W2560023338","https://openalex.org/W2612690371","https://openalex.org/W2798365772","https://openalex.org/W2962785568","https://openalex.org/W2962965870","https://openalex.org/W2963073614","https://openalex.org/W2963420272","https://openalex.org/W2963800363","https://openalex.org/W2964259004","https://openalex.org/W2982763192","https://openalex.org/W2985764327","https://openalex.org/W2991377405","https://openalex.org/W3011708679","https://openalex.org/W3034971973","https://openalex.org/W3035421056","https://openalex.org/W3043547428","https://openalex.org/W3096215947","https://openalex.org/W3103556460","https://openalex.org/W3118356434","https://openalex.org/W3196967582","https://openalex.org/W3199003182","https://openalex.org/W4226300688","https://openalex.org/W4246193833","https://openalex.org/W4280581786","https://openalex.org/W4288083516","https://openalex.org/W4297775537","https://openalex.org/W4312238440","https://openalex.org/W4312690709","https://openalex.org/W4312771828","https://openalex.org/W4312933868","https://openalex.org/W4319300595","https://openalex.org/W4385245566","https://openalex.org/W6726275242","https://openalex.org/W6737664043","https://openalex.org/W6743912273","https://openalex.org/W6756887525","https://openalex.org/W6765779288","https://openalex.org/W6767064347","https://openalex.org/W6785302134"],"related_works":["https://openalex.org/W2135359786","https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Image":[0],"Inpainting":[1],"methods":[2],"based":[3,56],"on":[4,57,183],"Neural":[5],"Networks":[6],"have":[7,25],"shown":[8],"an":[9],"impressive":[10],"improvement":[11],"in":[12,67,136,145,164,177,224,240],"the":[13,28,91,98,109,113,117,120,124,137,140,146,165,178,184,187,190,197,212,225,233],"past":[14],"few":[15],"years,":[16],"achieving":[17],"ever":[18],"more":[19,33],"convincing":[20],"results.":[21],"However,":[22],"these":[23,175],"improvements":[24],"come":[26],"at":[27],"cost":[29],"of":[30,93,119,123,142,189,235,257,259,262],"larger":[31,133],"and":[32,51,73,151,170,180,208,238,246,255,264],"computationally":[34],"intensive":[35],"models,":[36],"which":[37],"demand":[38],"powerful":[39],"hardware":[40],"for":[41,76,161],"their":[42],"execution.":[43],"In":[44],"this":[45],"paper":[46],"we":[47,86,127],"introduce":[48],"a":[49,132,217],"high-quality":[50],"lightweight":[52],"image":[53],"inpainting":[54],"architecture,":[55],"LaMa\u2019s":[58],"frequency":[59],"reconstruction":[60,141],"network,":[61],"using":[62,81],"Unconstrained":[63,82],"Blueprint":[64,83],"Separable":[65,84],"Convolutions,":[66],"combination":[68],"with":[69,131],"Relevant":[70],"Channel":[71],"Pruning":[72],"our":[74],"technique":[75],"Residual":[77],"Layer":[78],"Pruning.":[79],"By":[80,173],"Convolution,":[85],"manage":[87],"to":[88,116,205,210,221,244,249],"efficiently":[89],"reduce":[90,211],"number":[92,234,261],"model":[94,213,236,241],"weights":[95,130,158],"by":[96,231],"decorrelating":[97],"learned":[99],"knowledge.":[100],"We":[101],"also":[102],"observe":[103,128],"that":[104,129],"shallower":[105],"residual":[106],"layers":[107],"within":[108],"network\u2019s":[110],"bottleneck":[111],"are":[112,159],"main":[114],"contribution":[115],"quality":[118],"final":[121],"output":[122],"network.":[125],"Furthermore,":[126],"L1":[134,156],"norm":[135,157],"decoder":[138],"represent":[139],"lower-frequency":[143],"details":[144,163],"image,":[147,166],"such":[148,167],"as":[149,168],"color":[150],"coarse":[152],"structures,":[153],"while":[154,195,215],"smaller":[155],"responsible":[160],"high-frequency":[162],"edges":[169],"finer":[171],"textures.":[172],"pruning":[174],"components":[176],"network":[179,191],"performing":[181],"fine-tuning":[182],"remaining":[185],"weights,":[186],"performance":[188],"can":[192,202],"be":[193,203],"improved":[194],"reducing":[196],"required":[198],"resources.":[199],"Our":[200],"method":[201],"applied":[204],"encoder-decoder":[206],"architectures,":[207],"manages":[209],"complexity":[214],"maintaining":[216],"competitive":[218],"result":[219],"compared":[220,243],"other":[222],"models":[223,251,258],"literature.":[226],"The":[227],"proposed":[228],"architecture":[229],"reduces":[230],"72.89%":[232],"parameters,":[237],"65.80%":[239],"FLOPs":[242],"LaMa,":[245],"compares":[247],"favorably":[248],"state-of-the-art":[250],"when":[252],"measuring":[253],"FID":[254],"LPIPS":[256],"similar":[260],"parameters":[263],"complexity.":[265]},"counts_by_year":[],"updated_date":"2026-05-04T08:30:34.212998","created_date":"2025-10-10T00:00:00"}
