{"id":"https://openalex.org/W4292828912","doi":"https://doi.org/10.1109/cvprw56347.2022.00292","title":"Discriminability-enforcing loss to improve representation learning","display_name":"Discriminability-enforcing loss to improve representation learning","publication_year":2022,"publication_date":"2022-06-01","ids":{"openalex":"https://openalex.org/W4292828912","doi":"https://doi.org/10.1109/cvprw56347.2022.00292"},"language":"en","primary_location":{"id":"doi:10.1109/cvprw56347.2022.00292","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw56347.2022.00292","pdf_url":null,"source":{"id":"https://openalex.org/S4363607748","display_name":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","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 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5001612779","display_name":"Florinel-Alin Croitoru","orcid":"https://orcid.org/0009-0002-4187-4102"},"institutions":[{"id":"https://openalex.org/I141595442","display_name":"University of Bucharest","ror":"https://ror.org/02x2v6p15","country_code":"RO","type":"education","lineage":["https://openalex.org/I141595442"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Florinel-Alin Croitoru","raw_affiliation_strings":["University of Bucharest,Romania","University of Bucharest, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Bucharest,Romania","institution_ids":["https://openalex.org/I141595442"]},{"raw_affiliation_string":"University of Bucharest, Romania","institution_ids":["https://openalex.org/I141595442"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017536017","display_name":"Diana-Nicoleta Grigore","orcid":null},"institutions":[{"id":"https://openalex.org/I141595442","display_name":"University of Bucharest","ror":"https://ror.org/02x2v6p15","country_code":"RO","type":"education","lineage":["https://openalex.org/I141595442"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Diana-Nicoleta Grigore","raw_affiliation_strings":["University of Bucharest,Romania","University of Bucharest, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Bucharest,Romania","institution_ids":["https://openalex.org/I141595442"]},{"raw_affiliation_string":"University of Bucharest, Romania","institution_ids":["https://openalex.org/I141595442"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081017623","display_name":"Radu Tudor Ionescu","orcid":"https://orcid.org/0000-0002-9301-1950"},"institutions":[{"id":"https://openalex.org/I141595442","display_name":"University of Bucharest","ror":"https://ror.org/02x2v6p15","country_code":"RO","type":"education","lineage":["https://openalex.org/I141595442"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Radu Tudor Ionescu","raw_affiliation_strings":["University of Bucharest,Romania","University of Bucharest, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Bucharest,Romania","institution_ids":["https://openalex.org/I141595442"]},{"raw_affiliation_string":"University of Bucharest, Romania","institution_ids":["https://openalex.org/I141595442"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I141595442"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":null,"first_page":"2597","last_page":"2601"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9991999864578247,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8476760387420654},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.651603639125824},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6153671145439148},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5540710687637329},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.531195342540741},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5192484855651855},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5133916139602661},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4690423905849457},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.46759623289108276},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4148474633693695},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.0695231556892395}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8476760387420654},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.651603639125824},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6153671145439148},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5540710687637329},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.531195342540741},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5192484855651855},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5133916139602661},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4690423905849457},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.46759623289108276},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4148474633693695},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0695231556892395},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvprw56347.2022.00292","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw56347.2022.00292","pdf_url":null,"source":{"id":"https://openalex.org/S4363607748","display_name":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","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 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320327917","display_name":"Stiftung Mercator","ror":"https://ror.org/00q6ynx29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W154472438","https://openalex.org/W1849277567","https://openalex.org/W1959608418","https://openalex.org/W2096873754","https://openalex.org/W2100495367","https://openalex.org/W2155904486","https://openalex.org/W2163922914","https://openalex.org/W2194775991","https://openalex.org/W2520774990","https://openalex.org/W2726367589","https://openalex.org/W2753738274","https://openalex.org/W2842511635","https://openalex.org/W2896457183","https://openalex.org/W2951970475","https://openalex.org/W2953070460","https://openalex.org/W2963226019","https://openalex.org/W2963684088","https://openalex.org/W2970231061","https://openalex.org/W3005680577","https://openalex.org/W3036224891","https://openalex.org/W3118608800","https://openalex.org/W3209640737","https://openalex.org/W4214493665","https://openalex.org/W4287812705","https://openalex.org/W4288089799","https://openalex.org/W4297808394","https://openalex.org/W4300198444","https://openalex.org/W4320013936","https://openalex.org/W6600949241","https://openalex.org/W6606244218","https://openalex.org/W6638011348","https://openalex.org/W6640963894","https://openalex.org/W6674764686","https://openalex.org/W6685352114","https://openalex.org/W6687011383","https://openalex.org/W6718140377","https://openalex.org/W6726946684","https://openalex.org/W6733814495","https://openalex.org/W6744627333","https://openalex.org/W6755207826","https://openalex.org/W6764051988","https://openalex.org/W6769627184","https://openalex.org/W6774314701","https://openalex.org/W6776700526","https://openalex.org/W6779997284","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W1914651075","https://openalex.org/W1982774199","https://openalex.org/W2518599539","https://openalex.org/W2800691917","https://openalex.org/W2531896461","https://openalex.org/W2136516428","https://openalex.org/W2406522397","https://openalex.org/W2773500201","https://openalex.org/W2776636865","https://openalex.org/W2126384842"],"abstract_inverted_index":{"During":[0],"the":[1,11,21,24,29,40,44,52,57,70,78,81,91,106,109,113,116,127,131,172,177,185],"training":[2,173],"process,":[3],"deep":[4],"neural":[5,149],"networks":[6,154],"implicitly":[7],"learn":[8],"to":[9,56,90,125,158],"represent":[10],"input":[12],"data":[13,141],"samples":[14],"through":[15],"a":[16,64],"hierarchy":[17,25],"of":[18,23,31,43,84,108,115],"features,":[19,100],"where":[20],"size":[22],"is":[26,74],"determined":[27],"by":[28,51,69],"number":[30],"layers.":[32],"In":[33],"this":[34,60],"paper,":[35],"we":[36,62,120],"focus":[37],"on":[38,137],"enforcing":[39],"discriminative":[41,82],"power":[42],"high-level":[45,86,110],"representations,":[46],"that":[47,105,165],"are":[48],"typically":[49],"learned":[50],"deeper":[53],"layers":[54],"(closer":[55],"output).":[58],"To":[59],"end,":[61],"introduce":[63,121],"new":[65],"loss":[66,97,123,169],"term":[67,124],"inspired":[68],"Gini":[71,96],"impurity,":[72],"which":[73],"aimed":[75],"at":[76,188],"minimizing":[77],"entropy":[79],"(increasing":[80],"power)":[83],"individual":[85],"features":[87,111],"with":[88,180],"respect":[89],"class":[92],"labels.":[93],"Although":[94],"our":[95,167],"induces":[98],"highly-discriminative":[99],"it":[101],"does":[102],"not":[103],"ensure":[104],"distribution":[107,114],"matches":[112],"classes.":[117],"As":[118],"such,":[119],"another":[122],"minimize":[126],"Kullback\u2013Leibler":[128],"divergence":[129],"between":[130],"two":[132,138],"distributions.":[133],"We":[134],"conduct":[135],"experiments":[136],"image":[139],"classification":[140],"sets":[142],"(CIFAR-100":[143],"and":[144],"Caltech":[145],"101),":[146],"considering":[147],"multiple":[148],"architectures":[150],"ranging":[151],"from":[152],"convolutional":[153],"(ResNet-17,":[155],"ResNet-18,":[156],"ResNet-50)":[157],"transformers":[159],"(CvT).":[160],"Our":[161],"empirical":[162],"results":[163],"show":[164],"integrating":[166],"novel":[168],"terms":[170],"into":[171],"objective":[174],"consistently":[175],"out-performs":[176],"models":[178],"trained":[179],"cross-entropy":[181],"alone,":[182],"with-out":[183],"increasing":[184],"inference":[186],"time":[187],"all.":[189]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}