{"id":"https://openalex.org/W4385482877","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191936","title":"Learning Low-Rank Representations for Model Compression","display_name":"Learning Low-Rank Representations for Model Compression","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385482877","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191936"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10191936","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10191936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","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/A5006894022","display_name":"Zezhou Zhu","orcid":"https://orcid.org/0000-0002-0341-6584"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zezhou Zhu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056136429","display_name":"Yuan Dong","orcid":"https://orcid.org/0009-0004-8650-1603"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Dong","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047316686","display_name":"Zhao Zhong","orcid":"https://orcid.org/0000-0003-2758-706X"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhong Zhao","raw_affiliation_strings":["Central Media Technology Institute, Huawei,Shenzhen,China","Central Media Technology Institute, Huawei, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Central Media Technology Institute, Huawei,Shenzhen,China","institution_ids":["https://openalex.org/I2250955327"]},{"raw_affiliation_string":"Central Media Technology Institute, Huawei, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3942,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.54464514,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9990000128746033,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9970999956130981,"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/cluster-analysis","display_name":"Cluster analysis","score":0.8007601499557495},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.7184032201766968},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.583803653717041},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5675272345542908},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5573877096176147},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5486024022102356},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5463703870773315},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.4450760781764984},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3484978973865509},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3246793746948242},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.251289963722229},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.18381226062774658}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8007601499557495},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.7184032201766968},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.583803653717041},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5675272345542908},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5573877096176147},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5486024022102356},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5463703870773315},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4450760781764984},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3484978973865509},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3246793746948242},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.251289963722229},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.18381226062774658}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10191936","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10191936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","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":43,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W1724438581","https://openalex.org/W1902934009","https://openalex.org/W2117539524","https://openalex.org/W2124509324","https://openalex.org/W2147717514","https://openalex.org/W2194775991","https://openalex.org/W2233116163","https://openalex.org/W2524428287","https://openalex.org/W2894581376","https://openalex.org/W2955425717","https://openalex.org/W2960643108","https://openalex.org/W2962746461","https://openalex.org/W2962965870","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2963363373","https://openalex.org/W2963981420","https://openalex.org/W2964081807","https://openalex.org/W2964233199","https://openalex.org/W2982083293","https://openalex.org/W2997694134","https://openalex.org/W3001340915","https://openalex.org/W3105594518","https://openalex.org/W3127330041","https://openalex.org/W3177113534","https://openalex.org/W3198153656","https://openalex.org/W6637709462","https://openalex.org/W6639703010","https://openalex.org/W6677103964","https://openalex.org/W6689694917","https://openalex.org/W6724998850","https://openalex.org/W6726275242","https://openalex.org/W6727208969","https://openalex.org/W6755495546","https://openalex.org/W6762718338","https://openalex.org/W6765643347","https://openalex.org/W6766225098","https://openalex.org/W6772360584","https://openalex.org/W6783868215","https://openalex.org/W6784418643","https://openalex.org/W6789960662","https://openalex.org/W6800888104"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W4391160746","https://openalex.org/W1484111231","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W2121067345","https://openalex.org/W3089231081"],"abstract_inverted_index":{"Vector":[0,66],"Quantization":[1,67],"(VQ)":[2],"is":[3,93,117],"an":[4],"appealing":[5],"model":[6,13,52,192],"compression":[7,53,102,193,197],"method":[8],"to":[9,20,84],"obtain":[10,21],"a":[11,86,133,164],"tiny":[12],"with":[14,54,81,177,195],"less":[15],"accuracy":[16,116,186],"loss.":[17],"While":[18],"methods":[19],"better":[22],"codebooks":[23],"and":[24,60,113,139,142,148],"codes":[25],"under":[26],"fixed":[27],"clustering":[28,83,121,140],"dimensionality":[29,40,58,110,122],"have":[30],"been":[31],"extensively":[32],"studied,":[33],"optimizations":[34],"via":[35],"the":[36,55,101,109,114,153,156,189],"reduction":[37,59],"of":[38,57,89,111,155],"subvector":[39,82],"are":[41],"not":[42],"carefully":[43],"considered.":[44],"This":[45],"paper":[46],"reports":[47],"our":[48,99],"recent":[49],"progress":[50],"on":[51,179],"combination":[56],"vector":[61],"quantization,":[62],"proposing":[63],"Low-Rank":[64],"Representation":[65],"(LR":[68],"<sup":[69,74,173],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[70,75,124,130,159,167,174],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[71,76,175],"VQ).":[72],"LR":[73,172],"VQ":[77,176],"joins":[78],"low-rank":[79,136],"representation":[80],"construct":[85],"new":[87],"kind":[88],"building":[90],"block":[91],"that":[92,151],"optimized":[94],"by":[95,108,120],"end-to-end":[96],"training.":[97],"In":[98],"method,":[100],"ratio":[103],"could":[104],"be":[105],"directly":[106],"controlled":[107],"subvectors,":[112],"final":[115],"solely":[118],"determined":[119],"<tex":[123,129,158,166],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\tilde{d}$</tex>":[125,131,160,168],".":[126],"We":[127],"recognize":[128],"as":[132],"trade-off":[134],"between":[135],"approximation":[137],"error":[138,141],"carry":[143],"out":[144],"both":[145],"theoretical":[146],"analysis":[147],"experimental":[149],"observations":[150],"empower":[152],"estimation":[154],"proper":[157,165],"before":[161],"fine-tuning.":[162],"With":[163],",":[169],"we":[170],"evaluate":[171],"ResNet-18/ResNet-50":[178],"ImageNet":[180],"classification":[181],"datasets,":[182],"achieving":[183],"2.8%/1.0%":[184],"top-1":[185],"improvements":[187],"over":[188],"current":[190],"state-of-the-art":[191],"algorithms":[194],"43\u00d7/31\u00d7":[196],"factor.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}