{"id":"https://openalex.org/W3107330301","doi":"https://doi.org/10.1109/cisp-bmei51763.2020.9263625","title":"A De-redundant Network with Enhanced Classifier for Generalized Zero-Shot Learning","display_name":"A De-redundant Network with Enhanced Classifier for Generalized Zero-Shot Learning","publication_year":2020,"publication_date":"2020-10-17","ids":{"openalex":"https://openalex.org/W3107330301","doi":"https://doi.org/10.1109/cisp-bmei51763.2020.9263625","mag":"3107330301"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei51763.2020.9263625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei51763.2020.9263625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5018372874","display_name":"Jiayu Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiayu Ding","raw_affiliation_strings":["School of Electronics and Communication Engineering Guangzhou University,Guangzhou,China","School of Electronics and Communication Engineering Guangzhou University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Communication Engineering Guangzhou University,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]},{"raw_affiliation_string":"School of Electronics and Communication Engineering Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101435943","display_name":"Xiao Hu","orcid":"https://orcid.org/0000-0002-0251-828X"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Hu","raw_affiliation_strings":["School of Electronics and Communication Engineering Guangzhou University,Guangzhou,China","School of Electronics and Communication Engineering Guangzhou University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Communication Engineering Guangzhou University,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]},{"raw_affiliation_string":"School of Electronics and Communication Engineering Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037188516","display_name":"Junjiang Xiang","orcid":"https://orcid.org/0000-0001-5185-5322"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjiang Xiang","raw_affiliation_strings":["School of Electronics and Communication Engineering Guangzhou University,Guangzhou,China","School of Electronics and Communication Engineering Guangzhou University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Communication Engineering Guangzhou University,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]},{"raw_affiliation_string":"School of Electronics and Communication Engineering Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5018372874"],"corresponding_institution_ids":["https://openalex.org/I37987034"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15404704,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"253","last_page":"258"},"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":0.9995999932289124,"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":0.9995999932289124,"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/T10515","display_name":"Cancer-related molecular mechanisms research","score":0.9735999703407288,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9682000279426575,"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/discriminative-model","display_name":"Discriminative model","score":0.7991538047790527},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7385386824607849},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6692601442337036},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6528297662734985},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5287777185440063},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5192822813987732},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5178099870681763},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5126861333847046},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4164649546146393},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.37890899181365967}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7991538047790527},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7385386824607849},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6692601442337036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6528297662734985},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5287777185440063},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5192822813987732},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5178099870681763},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5126861333847046},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4164649546146393},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.37890899181365967},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei51763.2020.9263625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei51763.2020.9263625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2099471712","https://openalex.org/W2125389028","https://openalex.org/W2134270519","https://openalex.org/W2153579005","https://openalex.org/W2194775991","https://openalex.org/W2520774990","https://openalex.org/W2557579533","https://openalex.org/W2596142952","https://openalex.org/W2739748921","https://openalex.org/W2799215068","https://openalex.org/W2883127586","https://openalex.org/W2889143422","https://openalex.org/W2889587173","https://openalex.org/W2905461678","https://openalex.org/W2924476266","https://openalex.org/W2945291089","https://openalex.org/W2955087950","https://openalex.org/W2962879692","https://openalex.org/W2963283377","https://openalex.org/W2963955422","https://openalex.org/W2963960318","https://openalex.org/W2964121744","https://openalex.org/W2965638232","https://openalex.org/W2979571231","https://openalex.org/W2979728922","https://openalex.org/W2982407353","https://openalex.org/W2998124234","https://openalex.org/W3034730995","https://openalex.org/W3035297600","https://openalex.org/W3143107425","https://openalex.org/W4293469690","https://openalex.org/W4294170691","https://openalex.org/W4295521014","https://openalex.org/W4320013936","https://openalex.org/W6631190155","https://openalex.org/W6682691769","https://openalex.org/W6687483927","https://openalex.org/W6729906282","https://openalex.org/W6735913928","https://openalex.org/W6741832134","https://openalex.org/W6746671161","https://openalex.org/W6753532407","https://openalex.org/W6754700304","https://openalex.org/W6762466240","https://openalex.org/W6775369783","https://openalex.org/W6779364100"],"related_works":["https://openalex.org/W4396941953","https://openalex.org/W2093104230","https://openalex.org/W2987280934","https://openalex.org/W4390874210","https://openalex.org/W4384918963","https://openalex.org/W4365211920","https://openalex.org/W2128027845","https://openalex.org/W3014948380","https://openalex.org/W4386184937","https://openalex.org/W4241564561"],"abstract_inverted_index":{"Generalized":[0],"zero-shot":[1],"learning":[2],"(GZSL)":[3],"is":[4,118,123],"a":[5,34,95,105,111],"challenging":[6],"problem,":[7,91],"which":[8,68,115],"requires":[9],"the":[10,15,21,44,53,58,70,82,126,130,135,139,145,150],"model":[11,107,128,164],"to":[12,42,74,88,109,133,148],"classify":[13],"both":[14],"categories":[16,64],"that":[17,26,180],"have":[18,27],"appeared":[19],"in":[20,49,52,62,153,175],"training":[22],"set":[23],"and":[24,78,99,120,143,173],"those":[25],"not.":[28],"Existing":[29],"generative":[30],"models":[31,73],"usually":[32],"generate":[33,75],"large":[35],"number":[36],"of":[37,60,72,84],"samples":[38],"for":[39],"unseen":[40],"classes":[41],"tackle":[43,89],"serious":[45],"class":[46],"bias":[47],"problem":[48],"GZSL.":[50,85,176],"However,":[51],"existing":[54],"fine-grained":[55,167],"data":[56],"sets,":[57],"appearance":[59],"images":[61],"different":[63],"exhibit":[65],"slightly":[66],"differences,":[67],"challenges":[69],"ability":[71],"discriminative":[76],"examples":[77],"will":[79],"certainly":[80],"deteriorate":[81],"performance":[83],"In":[86],"order":[87],"this":[90,92],"paper":[93],"integrates":[94],"de-redundant":[96,131],"network":[97,132],"(DR)":[98],"an":[100],"enhanced":[101,146],"classifier":[102,147],"(EC)":[103],"into":[104],"new":[106],"(DR-EC)":[108],"learn":[110],"feature":[112,156],"space":[113,157],"on":[114,165],"redundant":[116,136],"information":[117,122,137,152],"removed":[119],"category":[121],"enhanced.":[124],"Specifically,":[125],"proposed":[127],"employs":[129],"remove":[134],"from":[138],"original":[140],"visual":[141],"features":[142],"uses":[144],"make":[149],"non-redundant":[151],"learned":[154],"redundancy-free":[155],"more":[158],"prominent.":[159],"We":[160],"evaluate":[161],"our":[162,181],"DR-EC":[163,182],"three":[166],"benchmark":[168],"datasets":[169],"(i.e.,":[170],"CUB,":[171],"SUN,":[172],"FLO)":[174],"The":[177],"results":[178],"show":[179],"approach":[183],"can":[184],"outperform":[185],"state-of-the-art":[186],"methods":[187],"with":[188],"significant":[189],"improvements.":[190]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}