{"id":"https://openalex.org/W2782645198","doi":"https://doi.org/10.1109/uemcon.2017.8248975","title":"Residual squeeze CNDS deep learning CNN model for very large scale places image recognition","display_name":"Residual squeeze CNDS deep learning CNN model for very large scale places image recognition","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2782645198","doi":"https://doi.org/10.1109/uemcon.2017.8248975","mag":"2782645198"},"language":"en","primary_location":{"id":"doi:10.1109/uemcon.2017.8248975","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon.2017.8248975","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","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/A5086227422","display_name":"Abhishek Verma","orcid":"https://orcid.org/0000-0001-6687-4809"},"institutions":[{"id":"https://openalex.org/I89554219","display_name":"New Jersey City University","ror":"https://ror.org/0546wew42","country_code":"US","type":"education","lineage":["https://openalex.org/I89554219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhishek Verma","raw_affiliation_strings":["Department of Computer Science, New Jersey City University, Jersey City, NJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, New Jersey City University, Jersey City, NJ","institution_ids":["https://openalex.org/I89554219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044780336","display_name":"Hussam Qassim","orcid":null},"institutions":[{"id":"https://openalex.org/I142934699","display_name":"California State University, Fullerton","ror":"https://ror.org/02avqqw26","country_code":"US","type":"education","lineage":["https://openalex.org/I142934699"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hussam Qassim","raw_affiliation_strings":["Department of Computer Science, California State University, Fullerton, California"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, California State University, Fullerton, California","institution_ids":["https://openalex.org/I142934699"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011391565","display_name":"David Feinzimer","orcid":null},"institutions":[{"id":"https://openalex.org/I142934699","display_name":"California State University, Fullerton","ror":"https://ror.org/02avqqw26","country_code":"US","type":"education","lineage":["https://openalex.org/I142934699"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Feinzimer","raw_affiliation_strings":["Department of Computer Science, California State University, Fullerton, California"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, California State University, Fullerton, California","institution_ids":["https://openalex.org/I142934699"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6466,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.79532341,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"463","last_page":"469"},"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":0.9980000257492065,"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":0.9970999956130981,"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/residual","display_name":"Residual","score":0.9331136345863342},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8390918374061584},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8079531192779541},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.781800389289856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6928307414054871},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5723255276679993},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5647439360618591},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49647289514541626},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4935234785079956},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43876853585243225},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.4199894070625305},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3843417465686798},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2593325972557068},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07972380518913269},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.05510452389717102}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.9331136345863342},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8390918374061584},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8079531192779541},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.781800389289856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6928307414054871},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5723255276679993},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5647439360618591},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49647289514541626},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4935234785079956},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43876853585243225},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.4199894070625305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3843417465686798},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2593325972557068},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07972380518913269},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.05510452389717102},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/uemcon.2017.8248975","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon.2017.8248975","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4099999964237213,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W1513618424","https://openalex.org/W1533861849","https://openalex.org/W1536680647","https://openalex.org/W1554663460","https://openalex.org/W1594587862","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W1932847118","https://openalex.org/W2017814585","https://openalex.org/W2025357764","https://openalex.org/W2064675550","https://openalex.org/W2097117768","https://openalex.org/W2102605133","https://openalex.org/W2107878631","https://openalex.org/W2109255472","https://openalex.org/W2111935653","https://openalex.org/W2117539524","https://openalex.org/W2117812871","https://openalex.org/W2131241448","https://openalex.org/W2134670479","https://openalex.org/W2147800946","https://openalex.org/W2155893237","https://openalex.org/W2157080539","https://openalex.org/W2163605009","https://openalex.org/W2168894214","https://openalex.org/W2179352600","https://openalex.org/W2194775991","https://openalex.org/W2206858481","https://openalex.org/W2271840356","https://openalex.org/W2276486856","https://openalex.org/W2279098554","https://openalex.org/W2328425223","https://openalex.org/W2415204304","https://openalex.org/W2531044305","https://openalex.org/W2563602643","https://openalex.org/W2613718673","https://openalex.org/W2949117887","https://openalex.org/W2952271367","https://openalex.org/W2953106684","https://openalex.org/W2963446712","https://openalex.org/W2963606038","https://openalex.org/W4300842377","https://openalex.org/W4388297464","https://openalex.org/W6620707391","https://openalex.org/W6631943919","https://openalex.org/W6635560335","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6676338569","https://openalex.org/W6677651945","https://openalex.org/W6678911119","https://openalex.org/W6679792166","https://openalex.org/W6684191040","https://openalex.org/W6684665197","https://openalex.org/W6687483927","https://openalex.org/W6688059459","https://openalex.org/W6694517276","https://openalex.org/W6695314431","https://openalex.org/W6716410670","https://openalex.org/W6725739302","https://openalex.org/W6728697212","https://openalex.org/W6780493881"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Deep":[0],"convolutional":[1,50,199],"neural":[2,51,200],"network":[3,26,52,66,201],"models":[4,59],"have":[5],"achieved":[6],"great":[7],"success":[8],"in":[9,125,130],"the":[10,14,19,40,61,91,97,101,105,119,131,139,143,166,189],"recent":[11],"years.":[12],"However,":[13],"optimization":[15],"of":[16,42,93,100,146],"size":[17,45,126],"and":[18,44,67,89,127,149,185],"time":[20],"needed":[21],"to":[22,117,164,175],"train":[23],"a":[24,28,48,160],"deep":[25,197],"is":[27,122],"research":[29],"area":[30],"that":[31,138],"needs":[32],"much":[33],"improvement.":[34],"In":[35,115,173],"this":[36],"paper,":[37],"we":[38,155],"address":[39],"issue":[41,92],"speed":[43],"by":[46],"proposing":[47],"compressed":[49],"model":[53,75,103,121,141,148],"namely":[54],"Residual":[55],"Squeeze":[56],"CNDS.":[57],"Proposed":[58],"compresses":[60],"earlier":[62],"very":[63,106],"successful":[64],"Residual-CNDS":[65,118,147],"further":[68,150],"improves":[69,151],"on":[70,104],"following":[71],"aspects:":[72],"(1)":[73],"small":[74],"size,":[76],"(2)":[77],"faster":[78,85,129],"speed,":[79],"(3)":[80],"uses":[81],"residual":[82,190],"learning":[83,191,198],"for":[84,169,192],"convergence,":[86],"better":[87],"generalization,":[88],"solves":[90],"degradation,":[94],"(4)":[95],"matches":[96],"recognition":[98],"accuracy":[99],"non-compressed":[102],"large-scale":[107],"grand":[108],"challenge":[109],"MIT":[110],"Places":[111],"365-Standard":[112],"scene":[113],"dataset.":[114],"comparison":[116,174],"proposed":[120,140,178],"87.64%":[123],"smaller":[124],"13.33%":[128],"training":[132],"time.":[133],"This":[134],"supports":[135],"our":[136,157,177],"claim":[137],"inherits":[142],"best":[144],"aspects":[145],"upon":[152],"it.":[153],"Moreover,":[154],"present":[156],"attempt":[158],"at":[159],"more":[161,182],"disciplined":[162],"approach":[163],"searching":[165],"design":[167],"space":[168],"novel":[170],"CNN":[171],"architectures.":[172],"SQUEEZENET":[176],"framework":[179],"can":[180],"be":[181],"easily":[183],"adapted":[184],"fully":[186],"integrated":[187],"with":[188],"compressing":[193],"various":[194],"other":[195],"contemporary":[196],"models.":[202]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
