{"id":"https://openalex.org/W2169448486","doi":null,"title":"Sample Distillation for Object Detection and Image Classification","display_name":"Sample Distillation for Object Detection and Image Classification","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2169448486","mag":"2169448486"},"language":"en","primary_location":{"id":"pmh:oai:infoscience.tind.io:203846","doi":null,"is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/203846","pdf_url":null,"source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I5124864","host_organization_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","host_organization_lineage":["https://openalex.org/I5124864"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference proceedings"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://infoscience.epfl.ch/record/203846","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008714951","display_name":"Olivier Can\u00e9vet","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I7495430","display_name":"Idiap Research Institute","ror":"https://ror.org/05932h694","country_code":"CH","type":"facility","lineage":["https://openalex.org/I7495430"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Olivier Can\u00e9vet","raw_affiliation_strings":["IDIAP RESEARCH INSTITUTE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IDIAP RESEARCH INSTITUTE","institution_ids":["https://openalex.org/I7495430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077836087","display_name":"Leonidas Lefakis","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I7495430","display_name":"Idiap Research Institute","ror":"https://ror.org/05932h694","country_code":"CH","type":"facility","lineage":["https://openalex.org/I7495430"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Leonidas Lefakis","raw_affiliation_strings":["IDIAP RESEARCH INSTITUTE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IDIAP RESEARCH INSTITUTE","institution_ids":["https://openalex.org/I7495430"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076094010","display_name":"Fran\u00e7ois Fleuret","orcid":"https://orcid.org/0000-0001-9457-7393","observed_orcids":["https://orcid.org/0000-0001-9457-7393"]},"institutions":[{"id":"https://openalex.org/I7495430","display_name":"Idiap Research Institute","ror":"https://ror.org/05932h694","country_code":"CH","type":"facility","lineage":["https://openalex.org/I7495430"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Fran\u00e7ois Fleuret","raw_affiliation_strings":["IDIAP RESEARCH INSTITUTE"],"raw_orcid":"https://orcid.org/0000-0001-9457-7393","affiliations":[{"raw_affiliation_string":"IDIAP RESEARCH INSTITUTE","institution_ids":["https://openalex.org/I7495430"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I7495430"],"apc_list":null,"apc_paid":null,"fwci":0.1936,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.33575058,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"64","last_page":"79"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.5473999977111816,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.5473999977111816,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.25119999051094055,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.1388999968767166,"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/training-set-reduction","display_name":"training data reduction","score":0.7229999899864197},{"id":"https://openalex.org/keywords/informative-sample-selection","display_name":"informative sample selection","score":0.7789999842643738},{"id":"https://openalex.org/keywords/bootstrap-method","display_name":"bootstrapping","score":0.7960000038146973},{"id":"https://openalex.org/keywords/object-detection","display_name":"object detection","score":0.9959999918937683},{"id":"https://openalex.org/keywords/image-classification","display_name":"image classification","score":0.9940000176429749},{"id":"https://openalex.org/keywords/large-scale-machine-learning","display_name":"large-scale machine learning","score":0.6460000276565552}],"concepts":[{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.8235290050506592},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6820784211158752},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6777967214584351},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.669367790222168},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5981945991516113},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5648991465568542},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.5577772855758667},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5384289026260376},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5268822312355042},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5066729187965393},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4932848811149597},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4249509572982788},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41851332783699036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3823641836643219},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2632724642753601},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.15781661868095398},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.07179582118988037},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:infoscience.tind.io:203846","doi":null,"is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/203846","pdf_url":null,"source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I5124864","host_organization_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","host_organization_lineage":["https://openalex.org/I5124864"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference proceedings"},{"id":"mag:2169448486","doi":null,"is_oa":false,"landing_page_url":"http://proceedings.mlr.press/v39/canevet14b.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306417739","display_name":"Asian Conference on Machine Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Asian Conference on Machine Learning","raw_type":null}],"best_oa_location":{"id":"pmh:oai:infoscience.tind.io:203846","doi":null,"is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/203846","pdf_url":null,"source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I5124864","host_organization_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","host_organization_lineage":["https://openalex.org/I5124864"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference proceedings"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W82130502","https://openalex.org/W1570592793","https://openalex.org/W1988790447","https://openalex.org/W2067191022","https://openalex.org/W2072419029","https://openalex.org/W2099664102","https://openalex.org/W2112796928","https://openalex.org/W2123602281","https://openalex.org/W2125896402","https://openalex.org/W2131320173","https://openalex.org/W2138689783","https://openalex.org/W2141125852","https://openalex.org/W2144506857","https://openalex.org/W2154436219","https://openalex.org/W2157452486","https://openalex.org/W2161969291","https://openalex.org/W2168356304","https://openalex.org/W3097096317"],"related_works":["https://openalex.org/W227501432","https://openalex.org/W2115895740","https://openalex.org/W4225151204","https://openalex.org/W4389040918","https://openalex.org/W2593534830","https://openalex.org/W4416712023","https://openalex.org/W3165535357","https://openalex.org/W4317761551","https://openalex.org/W4387914457","https://openalex.org/W3176459575"],"abstract_inverted_index":{"We":[0,67,108],"propose":[1],"a":[2,17,21,39,53,102,118,123],"novel":[3],"approach":[4,93],"to":[5,32,42,88,94,101],"efficiently":[6],"select":[7],"informative":[8],"samples":[9],"for":[10,74],"large-scale":[11],"learning.":[12],"Instead":[13],"of":[14,25,47,59,63,71,117,122],"directly":[15],"feeding":[16],"learning":[18],"algorithm":[19],"with":[20,105],"very":[22],"large":[23],"amount":[24],"samples,":[26],"as":[27],"it":[28],"is":[29],"usually":[30],"done":[31],"reach":[33],"state-of-the-art":[34],"performance,":[35],"we":[36,81,98],"have":[37],"developed":[38],"\"distillation\"":[40],"procedure":[41,73],"recursively":[43],"reduce":[44],"the":[45,57,60,64,69,90,115,129],"size":[46],"an":[48],"initial":[49],"training":[50],"set":[51,132],"using":[52,114],"criterion":[54],"that":[55,83,110],"ensures":[56],"maximization":[58],"information":[61],"content":[62],"selected":[65],"sub-set.":[66],"demonstrate":[68],"performance":[70,134],"this":[72],"two":[75],"different":[76],"computer":[77],"vision":[78],"problems.":[79],"First,":[80],"show":[82,109],"distillation":[84,100,119],"can":[85],"be":[86],"used":[87],"improve":[89],"traditional":[91],"bootstrapping":[92],"object":[95],"detection.":[96],"Second,":[97],"apply":[99],"classification":[103],"problem":[104],"artificial":[106],"distortions.":[107],"in":[111,128],"both":[112],"cases,":[113],"result":[116],"process":[120],"instead":[121],"random":[124],"sub-set":[125],"taken":[126],"uniformly":[127],"original":[130],"sample":[131],"improves":[133],"significantly.":[135]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-10-18T09:31:52.927876","created_date":"2016-06-24T00:00:00"}