{"id":"https://openalex.org/W4312796215","doi":"https://doi.org/10.1109/icpr56361.2022.9956548","title":"Conditional entropy minimization principle for learning domain invariant representation features","display_name":"Conditional entropy minimization principle for learning domain invariant representation features","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312796215","doi":"https://doi.org/10.1109/icpr56361.2022.9956548"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956548","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/A5114062732","display_name":"Thuan Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Thuan Nguyen","raw_affiliation_strings":["Tufts University,Department of Electrical and Computer Engineering,Medford,MA,02155"],"affiliations":[{"raw_affiliation_string":"Tufts University,Department of Electrical and Computer Engineering,Medford,MA,02155","institution_ids":["https://openalex.org/I121934306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086844652","display_name":"Boyang Lyu","orcid":"https://orcid.org/0000-0003-3295-0652"},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Boyang Lyu","raw_affiliation_strings":["Tufts University,Department of Electrical and Computer Engineering,Medford,MA,02155"],"affiliations":[{"raw_affiliation_string":"Tufts University,Department of Electrical and Computer Engineering,Medford,MA,02155","institution_ids":["https://openalex.org/I121934306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036913803","display_name":"Prakash Ishwar","orcid":"https://orcid.org/0000-0002-2621-1549"},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prakash Ishwar","raw_affiliation_strings":["Boston University,Department of Electrical and Computer Engineering,Boston,MA,02215"],"affiliations":[{"raw_affiliation_string":"Boston University,Department of Electrical and Computer Engineering,Boston,MA,02215","institution_ids":["https://openalex.org/I111088046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044523801","display_name":"Matthias Scheutz","orcid":"https://orcid.org/0000-0002-0064-2789"},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthias Scheutz","raw_affiliation_strings":["Tufts University,Department of Computer Science,Medford,MA,02155"],"affiliations":[{"raw_affiliation_string":"Tufts University,Department of Computer Science,Medford,MA,02155","institution_ids":["https://openalex.org/I121934306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004738943","display_name":"Shuchin Aeron","orcid":"https://orcid.org/0000-0002-1049-9795"},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuchin Aeron","raw_affiliation_strings":["Tufts University,Department of Electrical and Computer Engineering,Medford,MA,02155"],"affiliations":[{"raw_affiliation_string":"Tufts University,Department of Electrical and Computer Engineering,Medford,MA,02155","institution_ids":["https://openalex.org/I121934306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5114062732"],"corresponding_institution_ids":["https://openalex.org/I121934306"],"apc_list":null,"apc_paid":null,"fwci":0.8315,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.74827623,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3000","last_page":"3006"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T12676","display_name":"Machine Learning and ELM","score":0.993399977684021,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9692000150680542,"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/invariant","display_name":"Invariant (physics)","score":0.6680301427841187},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.6005190014839172},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.5547084212303162},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5208938121795654},{"id":"https://openalex.org/keywords/information-bottleneck-method","display_name":"Information bottleneck method","score":0.5068386197090149},{"id":"https://openalex.org/keywords/invariance-principle","display_name":"Invariance principle","score":0.47622138261795044},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4659114181995392},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44550928473472595},{"id":"https://openalex.org/keywords/conditional-entropy","display_name":"Conditional entropy","score":0.44163867831230164},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3941037356853485},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3758315145969391},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.3478991985321045},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2436503767967224},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.19898435473442078},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.13584214448928833}],"concepts":[{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.6680301427841187},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.6005190014839172},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.5547084212303162},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5208938121795654},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.5068386197090149},{"id":"https://openalex.org/C156387681","wikidata":"https://www.wikidata.org/wiki/Q6059484","display_name":"Invariance principle","level":2,"score":0.47622138261795044},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4659114181995392},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44550928473472595},{"id":"https://openalex.org/C101721835","wikidata":"https://www.wikidata.org/wiki/Q813908","display_name":"Conditional entropy","level":3,"score":0.44163867831230164},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3941037356853485},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3758315145969391},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.3478991985321045},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2436503767967224},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.19898435473442078},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.13584214448928833},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956548","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":62,"referenced_works":["https://openalex.org/W1562327244","https://openalex.org/W1686946872","https://openalex.org/W1731081199","https://openalex.org/W2012762214","https://openalex.org/W2114404076","https://openalex.org/W2123649031","https://openalex.org/W2131953535","https://openalex.org/W2149298154","https://openalex.org/W2797548502","https://openalex.org/W2889965839","https://openalex.org/W2900631671","https://openalex.org/W2963043696","https://openalex.org/W2998712190","https://openalex.org/W3035723985","https://openalex.org/W3035762155","https://openalex.org/W3044478874","https://openalex.org/W3092928935","https://openalex.org/W3094749184","https://openalex.org/W3108272348","https://openalex.org/W3109042658","https://openalex.org/W3118395365","https://openalex.org/W3119486842","https://openalex.org/W3133211101","https://openalex.org/W3133542152","https://openalex.org/W3134417895","https://openalex.org/W3162767959","https://openalex.org/W3173357892","https://openalex.org/W3191808899","https://openalex.org/W3206097584","https://openalex.org/W3208617561","https://openalex.org/W3211492676","https://openalex.org/W4287626607","https://openalex.org/W4287728573","https://openalex.org/W4288287305","https://openalex.org/W4289639938","https://openalex.org/W4293469690","https://openalex.org/W4309793942","https://openalex.org/W6633786245","https://openalex.org/W6637108112","https://openalex.org/W6637618735","https://openalex.org/W6729906282","https://openalex.org/W6754038005","https://openalex.org/W6765285020","https://openalex.org/W6773705132","https://openalex.org/W6779928312","https://openalex.org/W6780233385","https://openalex.org/W6781048204","https://openalex.org/W6783992515","https://openalex.org/W6784247468","https://openalex.org/W6785259824","https://openalex.org/W6786214372","https://openalex.org/W6787668776","https://openalex.org/W6788606385","https://openalex.org/W6791006536","https://openalex.org/W6791217402","https://openalex.org/W6791875987","https://openalex.org/W6796672888","https://openalex.org/W6797056055","https://openalex.org/W6799180490","https://openalex.org/W6802975297","https://openalex.org/W6803132187","https://openalex.org/W6803321516"],"related_works":["https://openalex.org/W2162899405","https://openalex.org/W2053566994","https://openalex.org/W4310557244","https://openalex.org/W202303397","https://openalex.org/W2279821256","https://openalex.org/W588585258","https://openalex.org/W2147544021","https://openalex.org/W415812316","https://openalex.org/W3204072549","https://openalex.org/W4286960213"],"abstract_inverted_index":{"Invariance-principle-based":[0],"methods":[1],"such":[2,21],"as":[3,11],"Invariant":[4],"Risk":[5],"Minimization":[6],"(IRM),":[7],"have":[8],"recently":[9],"emerged":[10],"promising":[12,19],"approaches":[13,22],"for":[14],"Domain":[15],"Generalization":[16],"(DG).":[17],"Despite":[18],"theory,":[20],"fail":[23],"in":[24],"common":[25],"classification":[26,109],"tasks":[27],"due":[28],"to":[29,58,65,83,112],"mixing":[30],"of":[31],"true":[32,102],"invariant":[33,37,62,103],"features":[34,38,63],"and":[35,90],"spurious":[36,61],"<sup":[39],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[40],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[41],".":[42],"To":[43],"address":[44],"this,":[45],"we":[46],"propose":[47],"a":[48,66,70],"framework":[49,89],"based":[50],"on":[51],"the":[52,60,84,101],"conditional":[53],"entropy":[54,96],"minimization":[55,97],"(CEM)":[56],"principle":[57],"filter-out":[59],"leading":[64],"new":[67],"algorithm":[68],"with":[69],"better":[71],"generalization":[72],"capability.":[73],"We":[74],"show":[75],"that":[76,92],"our":[77],"proposed":[78],"approach":[79,106],"is":[80],"closely":[81],"related":[82],"well-known":[85],"Information":[86],"Bottleneck":[87],"(IB)":[88],"prove":[91],"under":[93],"certain":[94],"assumptions,":[95],"can":[98],"exactly":[99],"recover":[100],"features.":[104],"Our":[105],"provides":[107],"competitive":[108],"accuracy":[110],"compared":[111],"recent":[113],"theoretically-principled":[114],"state-of-the-art":[115],"alternatives":[116],"across":[117],"several":[118],"DG":[119],"datasets.":[120]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}