{"id":"https://openalex.org/W3214206881","doi":"https://doi.org/10.1109/icccnt51525.2021.9580022","title":"Study of Overfitting through Activation Functions as a Hyper-parameter for Image Clothing Classification using Neural Network","display_name":"Study of Overfitting through Activation Functions as a Hyper-parameter for Image Clothing Classification using Neural Network","publication_year":2021,"publication_date":"2021-07-06","ids":{"openalex":"https://openalex.org/W3214206881","doi":"https://doi.org/10.1109/icccnt51525.2021.9580022","mag":"3214206881"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt51525.2021.9580022","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt51525.2021.9580022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/A5104114282","display_name":"Aritra Ray","orcid":null},"institutions":[{"id":"https://openalex.org/I106542073","display_name":"University of Calcutta","ror":"https://ror.org/01e7v7w47","country_code":"IN","type":"education","lineage":["https://openalex.org/I106542073"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Aritra Ray","raw_affiliation_strings":["A. K. C. School of Information Technology, University of Calcutta, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"A. K. C. School of Information Technology, University of Calcutta, Kolkata, India","institution_ids":["https://openalex.org/I106542073"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014253334","display_name":"Hena Ray","orcid":"https://orcid.org/0000-0001-8960-5687"},"institutions":[{"id":"https://openalex.org/I1331500379","display_name":"Centre for Development of Advanced Computing","ror":"https://ror.org/022abst40","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1331500379","https://openalex.org/I4210121746"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Hena Ray","raw_affiliation_strings":["Centre for Development of Advanced Computing, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"Centre for Development of Advanced Computing, Kolkata, India","institution_ids":["https://openalex.org/I1331500379"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5104114282"],"corresponding_institution_ids":["https://openalex.org/I106542073"],"apc_list":null,"apc_paid":null,"fwci":0.3843,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.61888889,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.9945999979972839,"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":0.9945999979972839,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9878000020980835,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.98555588722229},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.9019880890846252},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.8936101794242859},{"id":"https://openalex.org/keywords/activation-function","display_name":"Activation function","score":0.8080723881721497},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7579779624938965},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6735815405845642},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6569839119911194},{"id":"https://openalex.org/keywords/sigmoid-function","display_name":"Sigmoid function","score":0.6007701754570007},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5407116413116455},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5250442028045654},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5163020491600037},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49366530776023865},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.45792847871780396},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4283996820449829},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0988818109035492}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.98555588722229},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.9019880890846252},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8936101794242859},{"id":"https://openalex.org/C38365724","wikidata":"https://www.wikidata.org/wiki/Q4677469","display_name":"Activation function","level":3,"score":0.8080723881721497},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7579779624938965},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6735815405845642},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6569839119911194},{"id":"https://openalex.org/C81388566","wikidata":"https://www.wikidata.org/wiki/Q526668","display_name":"Sigmoid function","level":3,"score":0.6007701754570007},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5407116413116455},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5250442028045654},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5163020491600037},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49366530776023865},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.45792847871780396},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4283996820449829},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0988818109035492},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt51525.2021.9580022","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt51525.2021.9580022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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":7,"referenced_works":["https://openalex.org/W2527891130","https://openalex.org/W2793130633","https://openalex.org/W2972968629","https://openalex.org/W3011318476","https://openalex.org/W3013696689","https://openalex.org/W3047227759","https://openalex.org/W3134072853"],"related_works":["https://openalex.org/W2987302549","https://openalex.org/W3082263874","https://openalex.org/W3004759583","https://openalex.org/W3024979424","https://openalex.org/W4376118624","https://openalex.org/W4283785902","https://openalex.org/W3116943156","https://openalex.org/W3173926637","https://openalex.org/W4285326772","https://openalex.org/W3170224572"],"abstract_inverted_index":{"Often":[0],"neural":[1,82,111,141],"network":[2,112,142],"models":[3],"having":[4],"a":[5,14,85,110],"high":[6],"classification":[7,147],"accuracy":[8],"on":[9],"the":[10,30,41,50,61,64,71,76,90,115,118,121,129,153,161],"training":[11,42,67,93],"data":[12,43,54],"suffers":[13],"dent":[15],"when":[16,29],"deployed":[17],"in":[18,57,81,120],"real-world":[19],"scenarios":[20],"which":[21,143],"may":[22],"be":[23,133],"attributed":[24],"to":[25,48,87,100,132],"overfitting.":[26],"Overfitting":[27],"occurs":[28],"model":[31,65,119],"starts":[32],"taking":[33],"into":[34],"its":[35],"account":[36],"every":[37],"miniscule":[38],"detail":[39],"of":[40,63,78,104,117,123],"and":[44,94,152],"thus":[45],"becomes":[46],"incapable":[47],"generalize":[49],"learning":[51],"over":[52,148],"unknown":[53],"instances":[55],"resulting":[56],"many":[58],"misclassifications.":[59],"Tuning":[60],"hyper-parameters":[62],"while":[66,108],"can":[68,113],"help":[69,88],"bridge":[70],"gap.":[72],"This":[73,97],"paper":[74],"studies":[75],"contribution":[77],"activation":[79,106,137,158],"function":[80,138,159],"networks":[83],"as":[84],"hyper-parameter":[86],"estimate":[89],"gap":[91],"between":[92],"testing":[95,124],"accuracy.":[96,125],"predominantly":[98],"brings":[99],"light":[101],"that":[102],"use":[103],"different":[105],"functions":[107],"building":[109],"affect":[114],"performance":[116],"metric":[122],"Our":[126],"study":[127],"reflected":[128],"least":[130],"overfitting":[131],"demonstrated":[134],"by":[135,155],"softmax":[136],"for":[139],"our":[140],"performed":[144],"image":[145],"clothing":[146],"fashion":[149],"MNIST":[150],"dataset":[151],"maximal":[154],"hard":[156],"sigmoid":[157],"among":[160],"thirteen":[162],"experimented":[163],"ones.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
