{"id":"https://openalex.org/W4312381756","doi":"https://doi.org/10.1109/icpr56361.2022.9956371","title":"KRNet: Towards Efficient Knowledge Replay","display_name":"KRNet: Towards Efficient Knowledge Replay","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312381756","doi":"https://doi.org/10.1109/icpr56361.2022.9956371"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956371","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956371","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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/A5100422740","display_name":"Yingying Zhang","orcid":"https://orcid.org/0009-0000-8795-8171"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yingying Zhang","raw_affiliation_strings":["Hikvision Research Institute,Hangzhou,China","Hikvision Research Institute, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hikvision Research Institute,Hangzhou,China","institution_ids":[]},{"raw_affiliation_string":"Hikvision Research Institute, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047098807","display_name":"Qiaoyong Zhong","orcid":"https://orcid.org/0000-0002-5944-1818"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiaoyong Zhong","raw_affiliation_strings":["Hikvision Research Institute,Hangzhou,China","Hikvision Research Institute, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hikvision Research Institute,Hangzhou,China","institution_ids":[]},{"raw_affiliation_string":"Hikvision Research Institute, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100636142","display_name":"Di Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di Xie","raw_affiliation_strings":["Hikvision Research Institute,Hangzhou,China","Hikvision Research Institute, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hikvision Research Institute,Hangzhou,China","institution_ids":[]},{"raw_affiliation_string":"Hikvision Research Institute, Hangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085955762","display_name":"Shiliang Pu","orcid":"https://orcid.org/0000-0001-5269-7821"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shiliang Pu","raw_affiliation_strings":["Hikvision Research Institute,Hangzhou,China","Hikvision Research Institute, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hikvision Research Institute,Hangzhou,China","institution_ids":[]},{"raw_affiliation_string":"Hikvision Research Institute, Hangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100422740"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14495798,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":null,"first_page":"4772","last_page":"4778"},"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":0.9997000098228455,"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":0.9997000098228455,"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/T11609","display_name":"Geophysical Methods and Applications","score":0.9801999926567078,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T10036","display_name":"Advanced Neural Network 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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.902350664138794},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8331899642944336},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6650021076202393},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6558719873428345},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.5356884002685547},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5284019708633423},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5261275768280029},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5014636516571045},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4844124913215637},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42429807782173157},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4174841642379761},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4153413474559784},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.09287631511688232}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.902350664138794},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8331899642944336},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6650021076202393},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6558719873428345},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.5356884002685547},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5284019708633423},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5261275768280029},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5014636516571045},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4844124913215637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42429807782173157},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4174841642379761},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4153413474559784},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09287631511688232},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956371","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956371","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W16016350","https://openalex.org/W1522301498","https://openalex.org/W1682403713","https://openalex.org/W1959608418","https://openalex.org/W2100495367","https://openalex.org/W2117539524","https://openalex.org/W2147800946","https://openalex.org/W2154642048","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2560647685","https://openalex.org/W2734314755","https://openalex.org/W2771964490","https://openalex.org/W2884282566","https://openalex.org/W2948734064","https://openalex.org/W2963559848","https://openalex.org/W2963588172","https://openalex.org/W2964067969","https://openalex.org/W2964189064","https://openalex.org/W2981864462","https://openalex.org/W3011198096","https://openalex.org/W3046253174","https://openalex.org/W3096831136","https://openalex.org/W3118608800","https://openalex.org/W4250482878","https://openalex.org/W4287284742","https://openalex.org/W4289549433","https://openalex.org/W4295883599","https://openalex.org/W6631190155","https://openalex.org/W6638896900","https://openalex.org/W6640963894","https://openalex.org/W6677258307","https://openalex.org/W6738602802","https://openalex.org/W6746643698","https://openalex.org/W6755215958","https://openalex.org/W6774710049","https://openalex.org/W6787972765","https://openalex.org/W6791346067"],"related_works":["https://openalex.org/W2372020181","https://openalex.org/W2156531654","https://openalex.org/W1950940422","https://openalex.org/W1581723585","https://openalex.org/W4378714697","https://openalex.org/W2294330161","https://openalex.org/W4283822356","https://openalex.org/W2187606256","https://openalex.org/W2940472653","https://openalex.org/W2129146436"],"abstract_inverted_index":{"The":[0,19],"knowledge":[1,28,47,86],"replay":[2,34,48],"technique":[3],"has":[4],"been":[5],"widely":[6],"used":[7],"in":[8,22,58,140],"many":[9],"tasks":[10],"such":[11],"as":[12,133],"continual":[13,144],"learning":[14],"and":[15,33,67,84,117,132],"continuous":[16],"domain":[17],"adaptation.":[18],"key":[20],"lies":[21],"how":[23],"to":[24,45,98],"effectively":[25],"encode":[26],"the":[27,52,63,68,74,99,114,122,128,141],"extracted":[29],"from":[30],"previous":[31],"data":[32,66],"them":[35],"during":[36],"current":[37],"training":[38],"procedure.":[39],"A":[40],"simple":[41],"yet":[42],"effective":[43],"model":[44],"achieve":[46],"is":[49,71,137],"autoencoder.":[50],"However,":[51],"number":[53,97],"of":[54,65,130,143],"stored":[55],"latent":[56,115],"codes":[57,116],"autoencoder":[59],"increases":[60],"linearly":[61],"with":[62,103],"scale":[64],"trained":[69,120],"encoder":[70,123],"redundant":[72],"for":[73,113],"replaying":[75],"stage.":[76],"In":[77],"this":[78],"paper,":[79],"we":[80],"propose":[81],"a":[82,134],"novel":[83],"efficient":[85],"recording":[87],"network":[88],"(KRNet)":[89],"which":[90],"directly":[91],"maps":[92],"an":[93],"arbitrary":[94],"sample":[95],"identity":[96],"corresponding":[100],"datum.":[101],"Compared":[102],"autoencoder,":[104],"our":[105],"KRNet":[106],"requires":[107],"significantly":[108],"(400\u00d7)":[109],"less":[110],"storage":[111],"cost":[112],"can":[118],"be":[119],"without":[121],"sub-network.":[124],"Extensive":[125],"experiments":[126],"validate":[127],"efficiency":[129],"KRNet,":[131],"showcase,":[135],"it":[136],"successfully":[138],"applied":[139],"task":[142],"learning.":[145]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
