{"id":"https://openalex.org/W4395029315","doi":"https://doi.org/10.1109/tpami.2024.3392724","title":"Elodi: Ensemble Logit Difference Inhibition for Positive-Congruent Training","display_name":"Elodi: Ensemble Logit Difference Inhibition for Positive-Congruent Training","publication_year":2024,"publication_date":"2024-04-23","ids":{"openalex":"https://openalex.org/W4395029315","doi":"https://doi.org/10.1109/tpami.2024.3392724","pmid":"https://pubmed.ncbi.nlm.nih.gov/38652615"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2024.3392724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2024.3392724","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5004730674","display_name":"Yue Zhao","orcid":"https://orcid.org/0000-0003-2753-5921"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yue Zhao","raw_affiliation_strings":["AWS AI Labs, Seattle, WA, USA","Department of Computer Science, University of Texas at Austin, Texas, USA"],"raw_orcid":"https://orcid.org/0000-0003-2753-5921","affiliations":[{"raw_affiliation_string":"AWS AI Labs, Seattle, WA, USA","institution_ids":[]},{"raw_affiliation_string":"Department of Computer Science, University of Texas at Austin, Texas, USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102939275","display_name":"Yantao Shen","orcid":"https://orcid.org/0000-0001-5413-2445"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yantao Shen","raw_affiliation_strings":["AWS AI Labs, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0001-5413-2445","affiliations":[{"raw_affiliation_string":"AWS AI Labs, Seattle, WA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005925571","display_name":"Yuanjun Xiong","orcid":"https://orcid.org/0000-0002-6391-4921"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuanjun Xiong","raw_affiliation_strings":["AWS AI Labs, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0002-6391-4921","affiliations":[{"raw_affiliation_string":"AWS AI Labs, Seattle, WA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101564739","display_name":"Shuo Yang","orcid":"https://orcid.org/0000-0002-5754-3183"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuo Yang","raw_affiliation_strings":["AWS AI Labs, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0002-5754-3183","affiliations":[{"raw_affiliation_string":"AWS AI Labs, Seattle, WA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114859646","display_name":"Wei Xia","orcid":"https://orcid.org/0009-0004-1073-1533"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Xia","raw_affiliation_strings":["AWS AI Labs, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0009-0004-1073-1533","affiliations":[{"raw_affiliation_string":"AWS AI Labs, Seattle, WA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001760915","display_name":"Zhuowen Tu","orcid":"https://orcid.org/0000-0002-1900-2124"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhuowen Tu","raw_affiliation_strings":["AWS AI Labs, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0002-1900-2124","affiliations":[{"raw_affiliation_string":"AWS AI Labs, Seattle, WA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051534545","display_name":"Bernt Schiele","orcid":"https://orcid.org/0000-0001-9683-5237"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bernt Schiele","raw_affiliation_strings":["AWS AI Labs, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0001-9683-5237","affiliations":[{"raw_affiliation_string":"AWS AI Labs, Seattle, WA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038328783","display_name":"Stefano Soatto","orcid":"https://orcid.org/0000-0003-2902-6362"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stefano Soatto","raw_affiliation_strings":["AWS AI Labs, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0003-2902-6362","affiliations":[{"raw_affiliation_string":"AWS AI Labs, Seattle, WA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1731,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43865798,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"46","issue":"12","first_page":"7529","last_page":"7541"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9605000019073486,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9605000019073486,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11948","display_name":"Machine Learning in Materials Science","score":0.9469000101089478,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9456999897956848,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6409826874732971},{"id":"https://openalex.org/keywords/logit","display_name":"Logit","score":0.6098622679710388},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.6035551428794861},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.513221263885498},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49360787868499756},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4196087121963501},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41430792212486267},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.41337355971336365}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6409826874732971},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.6098622679710388},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6035551428794861},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.513221263885498},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49360787868499756},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4196087121963501},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41430792212486267},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.41337355971336365}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2024.3392724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2024.3392724","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:38652615","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38652615","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":73,"referenced_works":["https://openalex.org/W1603903339","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1821462560","https://openalex.org/W1975846642","https://openalex.org/W1988790447","https://openalex.org/W2006305154","https://openalex.org/W2008618334","https://openalex.org/W2095705004","https://openalex.org/W2108598243","https://openalex.org/W2115629999","https://openalex.org/W2117539524","https://openalex.org/W2118020555","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2281805729","https://openalex.org/W2331143823","https://openalex.org/W2338908902","https://openalex.org/W2622263826","https://openalex.org/W2739879705","https://openalex.org/W2747909401","https://openalex.org/W2797977484","https://openalex.org/W2905034244","https://openalex.org/W2963323244","https://openalex.org/W2963446712","https://openalex.org/W2980708516","https://openalex.org/W2987861506","https://openalex.org/W3014641072","https://openalex.org/W3034429256","https://openalex.org/W3034594226","https://openalex.org/W3034756453","https://openalex.org/W3081036064","https://openalex.org/W3104098829","https://openalex.org/W3124230025","https://openalex.org/W3167976421","https://openalex.org/W3170224286","https://openalex.org/W3177007952","https://openalex.org/W3177924560","https://openalex.org/W3178138002","https://openalex.org/W3196816907","https://openalex.org/W3197459106","https://openalex.org/W3217435001","https://openalex.org/W4212883601","https://openalex.org/W4246193833","https://openalex.org/W4400762160","https://openalex.org/W6631943919","https://openalex.org/W6637373629","https://openalex.org/W6637551013","https://openalex.org/W6638523607","https://openalex.org/W6674330103","https://openalex.org/W6685053522","https://openalex.org/W6703652217","https://openalex.org/W6714181750","https://openalex.org/W6730042731","https://openalex.org/W6730179637","https://openalex.org/W6737496325","https://openalex.org/W6739622702","https://openalex.org/W6739651123","https://openalex.org/W6748173244","https://openalex.org/W6755207826","https://openalex.org/W6757248479","https://openalex.org/W6762187592","https://openalex.org/W6764442487","https://openalex.org/W6767733985","https://openalex.org/W6771574350","https://openalex.org/W6780224944","https://openalex.org/W6784260353","https://openalex.org/W6784305470","https://openalex.org/W6787473312","https://openalex.org/W6796909962","https://openalex.org/W6800916835","https://openalex.org/W6803566340","https://openalex.org/W6810127701"],"related_works":["https://openalex.org/W2129863591","https://openalex.org/W2000361602","https://openalex.org/W1596769710","https://openalex.org/W1922851888","https://openalex.org/W2923497059","https://openalex.org/W2978363435","https://openalex.org/W4394896199","https://openalex.org/W2077865273","https://openalex.org/W2559245114","https://openalex.org/W1989125082"],"abstract_inverted_index":{"Negative":[0],"flips":[1,66],"are":[2,68],"errors":[3],"introduced":[4],"in":[5,57,81,110],"a":[6,10,35,93,103,121,127,131,146,161],"classification":[7,104,141,173],"system":[8,105],"when":[9],"legacy":[11],"model":[12,37,134,175],"is":[13,136],"updated.":[14],"Existing":[15],"methods":[16],"to":[17,38,72,101,130,138],"reduce":[18],"the":[19,28,40,53,73,82,89,117,140,157,166],"negative":[20,65],"flip":[21],"rate":[22,113],"(NFR)":[23],"either":[24],"do":[25],"so":[26],"at":[27,116],"expense":[29],"of":[30,55,120,160,163],"overall":[31],"accuracy":[32,181],"by":[33],"forcing":[34],"new":[36],"imitate":[39],"old":[41],"models,":[42],"or":[43],"use":[44],"ensembles,":[45],"which":[46,135,154],"multiply":[47],"inference":[48,118],"cost":[49,119],"prohibitively.":[50],"We":[51],"analyze":[52],"role":[54],"ensembles":[56],"reducing":[58],"NFR":[59,184],"and":[60,114,183],"observe":[61],"that":[62,67,106],"they":[63],"remove":[64],"typically":[69],"not":[70],"close":[71],"decision":[74],"boundary,":[75],"but":[76],"often":[77],"exhibit":[78],"large":[79],"deviations":[80],"distance":[83],"among":[84],"their":[85],"logits.":[86],"Based":[87],"on":[88],"observation,":[90],"we":[91],"present":[92],"method,":[94],"called":[95],"Ensemble":[96],"Logit":[97,150],"Difference":[98,151],"Inhibition":[99,152],"(ELODI),":[100],"train":[102],"achieves":[107],"paragon":[108],"performance":[109],"both":[111],"error":[112],"NFR,":[115],"single":[122,132],"model.":[123],"The":[124],"method":[125],"distills":[126],"homogeneous":[128],"ensemble":[129],"student":[133],"used":[137],"update":[139],"system.":[142],"ELODI":[143,178],"also":[144],"introduces":[145],"generalized":[147],"distillation":[148],"objective,":[149],"(LDI),":[153],"only":[155],"penalizes":[156],"logit":[158,168],"difference":[159],"subset":[162],"classes":[164],"with":[165,177],"highest":[167],"values.":[169],"On":[170],"multiple":[171],"image":[172],"benchmarks,":[174],"updates":[176],"demonstrate":[179],"superior":[180],"retention":[182],"reduction.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}