{"id":"https://openalex.org/W3090641261","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207705","title":"SkipConv: Skip Convolution for Computationally Efficient Deep CNNs","display_name":"SkipConv: Skip Convolution for Computationally Efficient Deep CNNs","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090641261","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207705","mag":"3090641261"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207705","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207705","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 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/A5007774595","display_name":"Pravendra Singh","orcid":"https://orcid.org/0000-0003-1001-2219"},"institutions":[{"id":"https://openalex.org/I94234084","display_name":"Indian Institute of Technology Kanpur","ror":"https://ror.org/05pjsgx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I94234084"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Pravendra Singh","raw_affiliation_strings":["Indian Institute of Technology Kanpur"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Kanpur","institution_ids":["https://openalex.org/I94234084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007109424","display_name":"Vinay P. Namboodiri","orcid":"https://orcid.org/0000-0001-5262-9722"},"institutions":[{"id":"https://openalex.org/I94234084","display_name":"Indian Institute of Technology Kanpur","ror":"https://ror.org/05pjsgx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I94234084"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vinay P. Namboodiri","raw_affiliation_strings":["Indian Institute of Technology Kanpur"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Kanpur","institution_ids":["https://openalex.org/I94234084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5007774595"],"corresponding_institution_ids":["https://openalex.org/I94234084"],"apc_list":null,"apc_paid":null,"fwci":0.5862,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.69395712,"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":"1","last_page":"8"},"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":0.9994000196456909,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9965999722480774,"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/convolution","display_name":"Convolution (computer science)","score":0.8554956912994385},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.8102496862411499},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7769914865493774},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7309430241584778},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5754784941673279},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.5680819153785706},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5404601097106934},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4958113133907318},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4423843026161194},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.43729403614997864},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4365791082382202},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4193205237388611},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39978328347206116},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.2969789206981659},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.28770607709884644},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21515372395515442},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12665346264839172}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.8554956912994385},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.8102496862411499},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7769914865493774},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7309430241584778},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5754784941673279},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.5680819153785706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5404601097106934},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4958113133907318},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4423843026161194},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.43729403614997864},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4365791082382202},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4193205237388611},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39978328347206116},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.2969789206981659},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28770607709884644},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21515372395515442},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12665346264839172},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/ijcnn48605.2020.9207705","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207705","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5199999809265137,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":70,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W2117539524","https://openalex.org/W2119144962","https://openalex.org/W2131747574","https://openalex.org/W2151103935","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2300242332","https://openalex.org/W2565639579","https://openalex.org/W2612445135","https://openalex.org/W2613718673","https://openalex.org/W2757143157","https://openalex.org/W2778053709","https://openalex.org/W2788715907","https://openalex.org/W2807961551","https://openalex.org/W2808168148","https://openalex.org/W2883111419","https://openalex.org/W2886851211","https://openalex.org/W2924515500","https://openalex.org/W2949941638","https://openalex.org/W2962723643","https://openalex.org/W2962835968","https://openalex.org/W2962956675","https://openalex.org/W2962965870","https://openalex.org/W2963118768","https://openalex.org/W2963125010","https://openalex.org/W2963145730","https://openalex.org/W2963163009","https://openalex.org/W2963363373","https://openalex.org/W2963382930","https://openalex.org/W2963446712","https://openalex.org/W2964065888","https://openalex.org/W2964233199","https://openalex.org/W2964266063","https://openalex.org/W2964299589","https://openalex.org/W2965955046","https://openalex.org/W2968025890","https://openalex.org/W2982536526","https://openalex.org/W2983868233","https://openalex.org/W2984005812","https://openalex.org/W2991375180","https://openalex.org/W3009270141","https://openalex.org/W3009383973","https://openalex.org/W3009958842","https://openalex.org/W3015315769","https://openalex.org/W3025217106","https://openalex.org/W3118608800","https://openalex.org/W4297775537","https://openalex.org/W6620707391","https://openalex.org/W6637373629","https://openalex.org/W6639102338","https://openalex.org/W6677580257","https://openalex.org/W6683411478","https://openalex.org/W6684191040","https://openalex.org/W6698200048","https://openalex.org/W6725739302","https://openalex.org/W6726275242","https://openalex.org/W6737664043","https://openalex.org/W6740474667","https://openalex.org/W6741047420","https://openalex.org/W6744702893","https://openalex.org/W6746996375","https://openalex.org/W6753040593","https://openalex.org/W6753490951","https://openalex.org/W6756199612","https://openalex.org/W6770574033","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W3005814217","https://openalex.org/W4288417960","https://openalex.org/W2921438861","https://openalex.org/W2995343971","https://openalex.org/W2982536526","https://openalex.org/W2964954556","https://openalex.org/W2992221004","https://openalex.org/W3034421924","https://openalex.org/W4386858688","https://openalex.org/W2952813363"],"abstract_inverted_index":{"Convolution":[0],"operation":[1,41,59,81],"in":[2,23,63,71,176,217],"deep":[3,25,102,218],"convolutional":[4],"neural":[5],"networks":[6],"is":[7,135],"the":[8,19,24,50,64,72,83,91,124,192],"most":[9],"computationally":[10],"expensive":[11],"as":[12,47,182,201],"compared":[13,48],"to":[14,28,49,173],"other":[15,198],"operations.":[16],"Most":[17],"of":[18,214],"model":[20,55,73,152,180],"computation":[21,46,76,85,125,162,175,216],"(FLOPS)":[22],"architecture":[26],"belong":[27],"convolution":[29,40,58,80,93,122,194],"operation.":[30],"In":[31],"this":[32],"paper,":[33],"we":[34,159],"are":[35,170],"proposing":[36],"a":[37,150,208],"novel":[38],"skip":[39,121,185],"that":[42,120,191],"employs":[43],"significantly":[44,126],"fewer":[45],"traditional":[51],"one":[52],"without":[53,68],"sacrificing":[54],"accuracy.":[56],"Skip":[57],"produces":[60],"structured":[61],"sparsity":[62,70],"output":[65],"feature":[66,88,129],"maps":[67],"requiring":[69],"parameters":[74],"for":[75,86,100,197],"reduction.":[77],"The":[78,132,144],"existing":[79],"performs":[82],"redundant":[84,95],"object":[87,202],"representation":[89],"while":[90,127],"proposed":[92,133,145,193],"skips":[94],"computation.":[96],"Our":[97],"empirical":[98],"evaluation":[99],"various":[101,111],"models":[103],"(VGG,":[104],"ResNet,":[105],"MobileNet,":[106],"and":[107,117,137,153,166,211],"Faster":[108],"R-CNN)":[109],"over":[110,141],"benchmarked":[112],"datasets":[113],"(CIFAR-10,":[114],"CIFAR-100,":[115],"ImageNet,":[116],"MS-COCO)":[118],"show":[119,189],"reduces":[123],"preserving":[128],"representational":[130],"capacity.":[131],"approach":[134,146],"model-agnostic":[136],"can":[138,206],"be":[139,207],"applied":[140],"any":[142],"architecture.":[143],"does":[147,154],"not":[148],"require":[149],"pretrained":[151],"train":[155],"from":[156],"scratch.":[157],"Hence":[158],"achieve":[160],"significant":[161],"reduction":[163],"at":[164],"training":[165],"test":[167],"time.":[168],"We":[169,187],"also":[171,188],"able":[172],"reduce":[174],"an":[177],"already":[178],"compact":[179],"such":[181,200],"MobileNet":[183],"using":[184],"convolution.":[186],"empirically":[190],"works":[195],"well":[196],"tasks":[199],"detection.":[203],"Therefore,":[204],"SkipConv":[205],"widely":[209],"usable":[210],"efficient":[212],"way":[213],"reducing":[215],"CNN":[219],"models.":[220]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}