{"id":"https://openalex.org/W4364381349","doi":"https://doi.org/10.1007/s10994-023-06326-9","title":"Understanding CNN fragility when learning with imbalanced data","display_name":"Understanding CNN fragility when learning with imbalanced data","publication_year":2023,"publication_date":"2023-04-11","ids":{"openalex":"https://openalex.org/W4364381349","doi":"https://doi.org/10.1007/s10994-023-06326-9"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-023-06326-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-023-06326-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06326-9.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06326-9.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000329853","display_name":"Damien Dablain","orcid":"https://orcid.org/0000-0003-0878-8560"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Damien Dablain","raw_affiliation_strings":["Department Computer Science and Engineering and Lucy Family Institute for Data and Society, University of Notre Dame, 10 Main, South Bend, IN, 46556, USA"],"affiliations":[{"raw_affiliation_string":"Department Computer Science and Engineering and Lucy Family Institute for Data and Society, University of Notre Dame, 10 Main, South Bend, IN, 46556, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009779001","display_name":"Kristen N. Jacobson","orcid":"https://orcid.org/0000-0002-8835-8028"},"institutions":[{"id":"https://openalex.org/I1288214837","display_name":"United States Naval Research Laboratory","ror":"https://ror.org/04d23a975","country_code":"US","type":"facility","lineage":["https://openalex.org/I1288214837","https://openalex.org/I1330347796","https://openalex.org/I175003984","https://openalex.org/I3130687028"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kristen N. Jacobson","raw_affiliation_strings":["AI Center, U.S. Naval Research Laboratory, 4555 Overlook Ave., Washington, DC, 20375, USA"],"affiliations":[{"raw_affiliation_string":"AI Center, U.S. Naval Research Laboratory, 4555 Overlook Ave., Washington, DC, 20375, USA","institution_ids":["https://openalex.org/I1288214837"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033365772","display_name":"Colin Bellinger","orcid":"https://orcid.org/0000-0002-3567-7834"},"institutions":[{"id":"https://openalex.org/I4210159778","display_name":"National Research Council Canada","ror":"https://ror.org/04mte1k06","country_code":"CA","type":"government","lineage":["https://openalex.org/I4210159778"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Colin Bellinger","raw_affiliation_strings":["NRC, National Research Council of Canada, 1200 Montreal Rd., Ottawa, ON, K1A 0R6, Canada"],"affiliations":[{"raw_affiliation_string":"NRC, National Research Council of Canada, 1200 Montreal Rd., Ottawa, ON, K1A 0R6, Canada","institution_ids":["https://openalex.org/I4210159778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101701349","display_name":"Mark Roberts","orcid":"https://orcid.org/0000-0003-2690-7658"},"institutions":[{"id":"https://openalex.org/I1288214837","display_name":"United States Naval Research Laboratory","ror":"https://ror.org/04d23a975","country_code":"US","type":"facility","lineage":["https://openalex.org/I1288214837","https://openalex.org/I1330347796","https://openalex.org/I175003984","https://openalex.org/I3130687028"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark Roberts","raw_affiliation_strings":["AI Center, U.S. Naval Research Laboratory, 4555 Overlook Ave., Washington, DC, 20375, USA"],"affiliations":[{"raw_affiliation_string":"AI Center, U.S. Naval Research Laboratory, 4555 Overlook Ave., Washington, DC, 20375, USA","institution_ids":["https://openalex.org/I1288214837"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068157871","display_name":"Nitesh V. Chawla","orcid":"https://orcid.org/0000-0003-3932-5956"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nitesh V. Chawla","raw_affiliation_strings":["Department Computer Science and Engineering and Lucy Family Institute for Data and Society, University of Notre Dame, 10 Main, South Bend, IN, 46556, USA"],"affiliations":[{"raw_affiliation_string":"Department Computer Science and Engineering and Lucy Family Institute for Data and Society, University of Notre Dame, 10 Main, South Bend, IN, 46556, USA","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5000329853"],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":10.6597,"has_fulltext":true,"cited_by_count":62,"citation_normalized_percentile":{"value":0.98773953,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"113","issue":"7","first_page":"4785","last_page":"4810"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9980999827384949,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9980999827384949,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9891999959945679,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9793999791145325,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6968642473220825},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6794853210449219},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6263040900230408},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6085440516471863},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5866111516952515},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5828855037689209},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5607069730758667},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5145565867424011},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45837923884391785},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42403650283813477}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6968642473220825},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6794853210449219},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6263040900230408},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6085440516471863},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5866111516952515},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5828855037689209},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5607069730758667},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5145565867424011},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45837923884391785},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42403650283813477},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10994-023-06326-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-023-06326-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06326-9.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:cisti-icist.nrc-cnrc.ca:cistinparc:af21be7a-27c3-4275-8427-41c90da5da1e","is_oa":true,"landing_page_url":"https://nrc-publications.canada.ca/eng/view/ft/?id=af21be7a-27c3-4275-8427-41c90da5da1e","pdf_url":null,"source":{"id":"https://openalex.org/S7407055245","display_name":"NPARC","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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s10994-023-06326-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-023-06326-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06326-9.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.4300000071525574}],"awards":[],"funders":[{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4364381349.pdf"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W1574715085","https://openalex.org/W1834627138","https://openalex.org/W1849277567","https://openalex.org/W2023639956","https://openalex.org/W2105464873","https://openalex.org/W2118978333","https://openalex.org/W2142402086","https://openalex.org/W2148143831","https://openalex.org/W2162950292","https://openalex.org/W2194775991","https://openalex.org/W2282821441","https://openalex.org/W2335728318","https://openalex.org/W2338318698","https://openalex.org/W2342792901","https://openalex.org/W2765177294","https://openalex.org/W2767106145","https://openalex.org/W2796885425","https://openalex.org/W2891503716","https://openalex.org/W2909961873","https://openalex.org/W2936503027","https://openalex.org/W2942630857","https://openalex.org/W2952120674","https://openalex.org/W2954503794","https://openalex.org/W2962858109","https://openalex.org/W2963212406","https://openalex.org/W2963351448","https://openalex.org/W2963749936","https://openalex.org/W2963860565","https://openalex.org/W2966679659","https://openalex.org/W2999905431","https://openalex.org/W3016970897","https://openalex.org/W3021199811","https://openalex.org/W3034175346","https://openalex.org/W3103934428","https://openalex.org/W3105260243","https://openalex.org/W3116286104","https://openalex.org/W3118608800","https://openalex.org/W3196684540","https://openalex.org/W4229494842","https://openalex.org/W4243367342","https://openalex.org/W4285483751","https://openalex.org/W4386897466","https://openalex.org/W6734194636","https://openalex.org/W6756040250","https://openalex.org/W6766985858","https://openalex.org/W6779357159","https://openalex.org/W6809695159","https://openalex.org/W7045329624"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2012531322","https://openalex.org/W4313906399","https://openalex.org/W4321487865","https://openalex.org/W2785900585","https://openalex.org/W2353730437","https://openalex.org/W2490303674","https://openalex.org/W2609066826","https://openalex.org/W2811106690","https://openalex.org/W2810752900"],"abstract_inverted_index":{"Abstract":[0],"Convolutional":[1],"neural":[2,127],"networks":[3],"(CNNs)":[4],"have":[5,16],"achieved":[6],"impressive":[7],"results":[8],"on":[9,55,60,164,201],"imbalanced":[10,56,254],"image":[11,245],"data,":[12,57],"but":[13],"they":[14],"still":[15],"difficulty":[17,222],"generalizing":[18,223],"to":[19,27,40,129,131,224],"minority":[20,41,132],"classes":[21,133],"and":[22,87,92,104,112,140,158,194,207,247],"their":[23,61,105,160],"decisions":[24,54],"are":[25,31],"difficult":[26],"interpret.":[28],"These":[29,95,197],"problems":[30],"related":[32],"because":[33],"the":[34,67,78,123,136,166,183,228,238],"method":[35],"by":[36],"which":[37,43,188],"CNNs":[38,65],"generalize":[39,130],"classes,":[42],"requires":[44],"improvement,":[45],"is":[46,83,169,179],"wrapped":[47],"in":[48,75,85,135,253],"a":[49,72,101,126,146,149,219],"black-box.":[50],"To":[51],"demystify":[52],"CNN":[53,147,220],"we":[58],"focus":[59,200],"latent":[62,176,213,233],"features.":[63,214],"Although":[64],"embed":[66],"pattern":[68],"knowledge":[69,82],"learned":[70],"from":[71,100],"training":[73,239],"set":[74],"model":[76,103,203],"parameters,":[77],"effect":[79],"of":[80,125,152,185,210,230],"this":[81],"contained":[84],"feature":[86],"classification":[88],"embeddings":[89,96],"(":[90],"FE":[91,141],"CE":[93,139,155],").":[94],"can":[97,114],"be":[98,115],"extracted":[99],"trained":[102],"global,":[106],"class":[107,137,153,168,177,186,205,212],"properties":[108],"(e.g.,":[109],"frequency,":[110],"magnitude":[111,229],"identity)":[113],"analyzed.":[116],"We":[117,143,173,215,241],"find":[118],"that":[119,145,159,175,218],"important":[120,181,190],"information":[121],"regarding":[122],"ability":[124],"network":[128],"resides":[134],"top-K":[138,154,232],".":[142],"show":[144],"learns":[148],"limited":[150],"number":[151,184],"per":[156],"category,":[157],"magnitudes":[161],"vary":[162],"based":[163],"whether":[165],"same":[167],"balanced":[170],"or":[171],"imbalanced.":[172],"hypothesize":[174],"diversity":[178],"as":[180,182],"examples,":[187],"has":[189,221],"implications":[191],"for":[192,256],"re-sampling":[193],"cost-sensitive":[195,249],"methods.":[196],"methods":[198],"generally":[199],"rebalancing":[202],"weights,":[204],"numbers":[206],"margins;":[208],"instead":[209],"diversifying":[211],"also":[216],"demonstrate":[217],"test":[225],"data":[226],"if":[227],"its":[231],"features":[234],"do":[235],"not":[236],"match":[237],"set.":[240],"use":[242],"three":[243],"popular":[244],"datasets":[246],"two":[248],"algorithms":[250],"commonly":[251],"employed":[252],"learning":[255],"our":[257],"experiments.":[258]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":27},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":4}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}