{"id":"https://openalex.org/W3174581454","doi":"https://doi.org/10.1145/3457682.3457699","title":"Semantic Auto-Encoder with L2-norm Constraint for Zero-Shot Learning","display_name":"Semantic Auto-Encoder with L2-norm Constraint for Zero-Shot Learning","publication_year":2021,"publication_date":"2021-02-26","ids":{"openalex":"https://openalex.org/W3174581454","doi":"https://doi.org/10.1145/3457682.3457699","mag":"3174581454"},"language":"en","primary_location":{"id":"doi:10.1145/3457682.3457699","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3457682.3457699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 13th International Conference on Machine Learning and Computing","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/A5100731116","display_name":"Yuhao Wu","orcid":"https://orcid.org/0000-0001-9155-3484"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuhao Wu","raw_affiliation_strings":["Shenzhen University, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028260055","display_name":"Weipeng Cao","orcid":"https://orcid.org/0000-0003-2414-6066"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weipeng Cao","raw_affiliation_strings":["Shenzhen University, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100346550","display_name":"Liu Ye","orcid":"https://orcid.org/0000-0002-2830-2049"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Liu","raw_affiliation_strings":["Shenzhen University, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100633973","display_name":"Zhong Ming","orcid":"https://orcid.org/0000-0001-9310-3460"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhong Ming","raw_affiliation_strings":["Shenzhen University, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100393871","display_name":"Jianqiang Li","orcid":"https://orcid.org/0000-0002-2208-962X"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianqiang Li","raw_affiliation_strings":["Shenzhen University, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101776400","display_name":"Bo L\u00fc","orcid":"https://orcid.org/0000-0003-0471-7546"},"institutions":[{"id":"https://openalex.org/I106994412","display_name":"Sinopec (China)","ror":"https://ror.org/0161q6d74","country_code":"CN","type":"company","lineage":["https://openalex.org/I106994412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Lu","raw_affiliation_strings":["Southwest Oil and Natural Gas Branch of Sinopec, China"],"affiliations":[{"raw_affiliation_string":"Southwest Oil and Natural Gas Branch of Sinopec, China","institution_ids":["https://openalex.org/I106994412"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100731116"],"corresponding_institution_ids":["https://openalex.org/I180726961"],"apc_list":null,"apc_paid":null,"fwci":0.8158,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77855974,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"12","issue":null,"first_page":"101","last_page":"105"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9779999852180481,"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/T12676","display_name":"Machine Learning and ELM","score":0.9746999740600586,"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/autoencoder","display_name":"Autoencoder","score":0.7557446956634521},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6510739326477051},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5962533950805664},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.5315734148025513},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5240989923477173},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.499525785446167},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.49157699942588806},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4816536009311676},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44628119468688965},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.39344102144241333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37780091166496277},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22518393397331238},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.20677608251571655}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7557446956634521},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6510739326477051},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5962533950805664},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.5315734148025513},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5240989923477173},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.499525785446167},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.49157699942588806},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4816536009311676},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44628119468688965},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.39344102144241333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37780091166496277},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22518393397331238},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.20677608251571655},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3457682.3457699","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3457682.3457699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 13th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7097245526","display_name":null,"funder_award_id":"61836005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"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":23,"referenced_works":["https://openalex.org/W1676314349","https://openalex.org/W1703030490","https://openalex.org/W1960364170","https://openalex.org/W2032699694","https://openalex.org/W2044913453","https://openalex.org/W2088748973","https://openalex.org/W2097117768","https://openalex.org/W2098411764","https://openalex.org/W2119874464","https://openalex.org/W2128532956","https://openalex.org/W2145215286","https://openalex.org/W2152411181","https://openalex.org/W2289084343","https://openalex.org/W2552383788","https://openalex.org/W2596142952","https://openalex.org/W2604272474","https://openalex.org/W2611632661","https://openalex.org/W2963325024","https://openalex.org/W2964086552","https://openalex.org/W3100093508","https://openalex.org/W4236965008","https://openalex.org/W4241307704","https://openalex.org/W6664298863"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W2909431601","https://openalex.org/W4294770367"],"abstract_inverted_index":{"Zero-Shot":[0],"Learning":[1],"(ZSL)":[2],"is":[3,32],"an":[4],"effective":[5],"paradigm":[6],"to":[7,58,86,115],"solve":[8,92],"label":[9],"prediction":[10],"when":[11],"some":[12],"classes":[13],"have":[14,24],"no":[15],"training":[16],"samples.":[17],"In":[18,69],"recent":[19],"years,":[20],"many":[21],"ZSL":[22,155],"algorithms":[23],"been":[25],"proposed.":[26],"Among":[27],"them,":[28],"semantic":[29,67],"autoencoder":[30],"(SAE)":[31],"widely":[33],"used":[34],"because":[35],"of":[36,49,81,130],"its":[37,120],"simplicity":[38],"and":[39,66,84,153],"good":[40],"generalization":[41],"ability.":[42],"However,":[43],"our":[44,141],"research":[45],"found":[46],"that":[47,140],"most":[48],"the":[50,60,71,78,82,88,102,111,116,126,131,149],"existing":[51],"SAE":[52,99,151],"based":[53],"methods":[54],"use":[55],"implicit":[56,72],"constraints":[57,73],"guarantee":[59],"mapping":[61,117],"quality":[62],"between":[63],"feature":[64],"space":[65],"space.":[68],"fact,":[70],"are":[74],"insufficient":[75],"in":[76,106,119],"minimizing":[77],"structural":[79,127],"risk":[80,128],"model":[83,152],"easy":[85],"cause":[87],"over-fitting":[89],"problem.":[90],"To":[91],"this":[93,107],"problem,":[94],"we":[95],"propose":[96],"a":[97],"novel":[98],"algorithm":[100],"with":[101],"L2-norm":[103],"constraint":[104,114],"(SAE-L2)":[105],"study.":[108],"SAE-L2":[109,143],"adds":[110],"L2":[112],"regularization":[113],"parameters":[118],"optimization":[121],"objective,":[122],"which":[123],"explicitly":[124],"guarantees":[125],"minimization":[129],"model.":[132],"Extensive":[133],"experiments":[134],"on":[135],"four":[136],"benchmark":[137],"datasets":[138],"show":[139],"proposed":[142],"can":[144],"achieve":[145],"better":[146],"performance":[147],"than":[148],"original":[150],"other":[154],"algorithms.":[156]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}