{"id":"https://openalex.org/W4384625636","doi":"https://doi.org/10.1145/3539618.3592001","title":"Improved Vector Quantization For Dense Retrieval with Contrastive Distillation","display_name":"Improved Vector Quantization For Dense Retrieval with Contrastive Distillation","publication_year":2023,"publication_date":"2023-07-18","ids":{"openalex":"https://openalex.org/W4384625636","doi":"https://doi.org/10.1145/3539618.3592001"},"language":"en","primary_location":{"id":"doi:10.1145/3539618.3592001","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3592001","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","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/A5108832334","display_name":"James O\u2019 Neill","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"James O' Neill","raw_affiliation_strings":["Huawei, Dublin, Ireland"],"raw_orcid":"https://orcid.org/0000-0002-5196-7719","affiliations":[{"raw_affiliation_string":"Huawei, Dublin, Ireland","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053023517","display_name":"Sourav Dutta","orcid":"https://orcid.org/0000-0002-8934-9166"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sourav Dutta","raw_affiliation_strings":["Huawei, Dublin, Ireland"],"raw_orcid":"https://orcid.org/0000-0002-8934-9166","affiliations":[{"raw_affiliation_string":"Huawei, Dublin, Ireland","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3368,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.57764922,"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":"2072","last_page":"2076"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9988999962806702,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9988999962806702,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9979000091552734,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9973999857902527,"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/computer-science","display_name":"Computer science","score":0.7042173147201538},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.6794506311416626},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6566709280014038},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.6055225133895874},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5351909399032593},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.5127493143081665},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.473989874124527},{"id":"https://openalex.org/keywords/learning-vector-quantization","display_name":"Learning vector quantization","score":0.45265573263168335},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.42191070318222046},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3899731934070587},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.333566814661026},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21271154284477234},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.18329420685768127},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.0979006290435791},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.082461416721344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7042173147201538},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.6794506311416626},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6566709280014038},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.6055225133895874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5351909399032593},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.5127493143081665},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.473989874124527},{"id":"https://openalex.org/C40567965","wikidata":"https://www.wikidata.org/wiki/Q1820283","display_name":"Learning vector quantization","level":3,"score":0.45265573263168335},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.42191070318222046},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3899731934070587},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.333566814661026},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21271154284477234},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.18329420685768127},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0979006290435791},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.082461416721344},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"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:10.1145/3539618.3592001","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3592001","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.8399999737739563,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1981276685","https://openalex.org/W2119913432","https://openalex.org/W2124509324","https://openalex.org/W2132234208","https://openalex.org/W2887783173","https://openalex.org/W2913954081","https://openalex.org/W2963213349","https://openalex.org/W2998702515","https://openalex.org/W3099700870","https://openalex.org/W3205509771","https://openalex.org/W3209791570","https://openalex.org/W3212208733","https://openalex.org/W4284685333"],"related_works":["https://openalex.org/W2100968651","https://openalex.org/W4243803532","https://openalex.org/W1530525041","https://openalex.org/W2352648934","https://openalex.org/W4230688072","https://openalex.org/W1915693853","https://openalex.org/W2378212145","https://openalex.org/W2798892016","https://openalex.org/W2202992072","https://openalex.org/W2013592766"],"abstract_inverted_index":{"Recent":[0],"work":[1,68,88],"has":[2],"identified":[3],"that":[4,39,54,107,126],"distillation":[5,96,100,128],"can":[6,46],"be":[7,47],"used":[8,48],"to":[9],"create":[10],"vector":[11,137],"quantization":[12],"based":[13],"ANN":[14],"indexes":[15],"by":[16,43,97],"learning":[17,94],"the":[18,40,44,51,71,90,102,108,134],"inverted":[19],"file":[20],"index":[21],"and":[22,36,82,95,119],"product":[23,83],"quantization.":[24],"The":[25],"argued":[26],"advantage":[27],"of":[28,50,75,92,133],"using":[29,58,98],"a":[30],"fixed":[31],"teacher":[32,45,72,103],"encoder":[33,73],"for":[34,136],"queries":[35],"documents":[37],"is":[38],"scores":[41,106],"produced":[42],"instead":[49],"label":[52],"judgements":[53],"are":[55],"required":[56],"when":[57],"traditional":[59],"supervised":[60],"learning,":[61],"such":[62],"as":[63],"contrastive":[64,93,99,105,127],"learning.":[65],"However,":[66],"current":[67,131],"only":[69],"distills":[70],"outputs":[74,104],"dot":[76],"products":[77],"between":[78],"quantized":[79,84,138],"query":[80],"embedddings":[81],"document":[85],"embeddings.":[86],"Our":[87,112],"combines":[89],"benefits":[91],"whereby":[101],"student":[109],"learns":[110],"from.":[111],"experimental":[113],"results":[114],"on":[115],"MSMARCO":[116],"passage":[117],"retrieval":[118],"NQ":[120],"open":[121],"question":[122],"answering":[123],"datasets":[124],"show":[125],"improves":[129],"over":[130],"state":[132],"art":[135],"dense":[139],"retrieval.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}