{"id":"https://openalex.org/W4413791360","doi":"https://doi.org/10.1007/s10115-025-02563-7","title":"Focal Sampling: SGD biased towards early important samples for efficient image classification with augmentation selection","display_name":"Focal Sampling: SGD biased towards early important samples for efficient image classification with augmentation selection","publication_year":2025,"publication_date":"2025-08-28","ids":{"openalex":"https://openalex.org/W4413791360","doi":"https://doi.org/10.1007/s10115-025-02563-7"},"language":"en","primary_location":{"id":"doi:10.1007/s10115-025-02563-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10115-025-02563-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10115-025-02563-7.pdf","source":{"id":"https://openalex.org/S81770430","display_name":"Knowledge and Information Systems","issn_l":"0219-1377","issn":["0219-1377","0219-3116"],"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":"Knowledge and Information Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10115-025-02563-7.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012035871","display_name":"Alessio Quercia","orcid":null},"institutions":[{"id":"https://openalex.org/I171892758","display_name":"Forschungszentrum J\u00fclich","ror":"https://ror.org/02nv7yv05","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I171892758"]},{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Alessio Quercia","raw_affiliation_strings":["Department of Computer Science, RWTH Aachen University, Aachen, Germany","Institute for Advanced Simulation, IAS-8: Data Analytics and Machine Learning, Forschungszentrum Juelich, Juelich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]},{"raw_affiliation_string":"Institute for Advanced Simulation, IAS-8: Data Analytics and Machine Learning, Forschungszentrum Juelich, Juelich, Germany","institution_ids":["https://openalex.org/I171892758"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Fernanda Nader","orcid":null},"institutions":[{"id":"https://openalex.org/I171892758","display_name":"Forschungszentrum J\u00fclich","ror":"https://ror.org/02nv7yv05","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I171892758"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fernanda Nader","raw_affiliation_strings":["Institute for Advanced Simulation, IAS-8: Data Analytics and Machine Learning, Forschungszentrum Juelich, Juelich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Advanced Simulation, IAS-8: Data Analytics and Machine Learning, Forschungszentrum Juelich, Juelich, Germany","institution_ids":["https://openalex.org/I171892758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073083912","display_name":"Abigail Morrison","orcid":"https://orcid.org/0000-0001-6933-797X"},"institutions":[{"id":"https://openalex.org/I171892758","display_name":"Forschungszentrum J\u00fclich","ror":"https://ror.org/02nv7yv05","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I171892758"]},{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Abigail Morrison","raw_affiliation_strings":["Department of Computer Science, RWTH Aachen University, Aachen, Germany","Institute for Advanced Simulation, IAS-6: Computational and Systems Neuroscience, Forschungszentrum Juelich, Juelich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]},{"raw_affiliation_string":"Institute for Advanced Simulation, IAS-6: Computational and Systems Neuroscience, Forschungszentrum Juelich, Juelich, Germany","institution_ids":["https://openalex.org/I171892758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058673315","display_name":"Hanno Scharr","orcid":"https://orcid.org/0000-0002-8555-6416"},"institutions":[{"id":"https://openalex.org/I171892758","display_name":"Forschungszentrum J\u00fclich","ror":"https://ror.org/02nv7yv05","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I171892758"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hanno Scharr","raw_affiliation_strings":["Institute for Advanced Simulation, IAS-8: Data Analytics and Machine Learning, Forschungszentrum Juelich, Juelich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Advanced Simulation, IAS-8: Data Analytics and Machine Learning, Forschungszentrum Juelich, Juelich, Germany","institution_ids":["https://openalex.org/I171892758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104360871","display_name":"Ira Assent","orcid":"https://orcid.org/0000-0002-1091-9948"},"institutions":[{"id":"https://openalex.org/I171892758","display_name":"Forschungszentrum J\u00fclich","ror":"https://ror.org/02nv7yv05","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I171892758"]},{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DE","DK"],"is_corresponding":false,"raw_author_name":"Ira Assent","raw_affiliation_strings":["Department of Computer Science, Aarhus University, Aarhus, Denmark","Institute for Advanced Simulation, IAS-8: Data Analytics and Machine Learning, Forschungszentrum Juelich, Juelich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Aarhus University, Aarhus, Denmark","institution_ids":["https://openalex.org/I204337017"]},{"raw_affiliation_string":"Institute for Advanced Simulation, IAS-8: Data Analytics and Machine Learning, Forschungszentrum Juelich, Juelich, Germany","institution_ids":["https://openalex.org/I171892758"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5012035871"],"corresponding_institution_ids":["https://openalex.org/I171892758","https://openalex.org/I887968799"],"apc_list":{"value":2890,"currency":"USD","value_usd":2890},"apc_paid":{"value":2890,"currency":"USD","value_usd":2890},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08616891,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"67","issue":"11","first_page":"11161","last_page":"11191"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9993000030517578,"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/computer-science","display_name":"Computer science","score":0.7730559706687927},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.7626010775566101},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.623515248298645},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5976975560188293},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5852919220924377},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.584622323513031},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5501450300216675},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5068677067756653},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5020909309387207},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.465084969997406},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.44087710976600647},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.42186301946640015},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.42050617933273315},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.4162788689136505},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41452085971832275},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34197837114334106},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.24966871738433838},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14075741171836853},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10894039273262024}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7730559706687927},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.7626010775566101},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.623515248298645},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5976975560188293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5852919220924377},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.584622323513031},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5501450300216675},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5068677067756653},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5020909309387207},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.465084969997406},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.44087710976600647},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.42186301946640015},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.42050617933273315},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.4162788689136505},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41452085971832275},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34197837114334106},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.24966871738433838},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14075741171836853},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10894039273262024},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","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/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1007/s10115-025-02563-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10115-025-02563-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10115-025-02563-7.pdf","source":{"id":"https://openalex.org/S81770430","display_name":"Knowledge and Information Systems","issn_l":"0219-1377","issn":["0219-1377","0219-3116"],"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":"Knowledge and Information Systems","raw_type":"journal-article"},{"id":"pmh:oai:publications.rwth-aachen.de:1026963","is_oa":true,"landing_page_url":"https://publications.rwth-aachen.de/record/1026963","pdf_url":null,"source":{"id":"https://openalex.org/S4306401033","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Knowledge and information systems 67(11), 11161-11191 (2025). doi:10.1007/s10115-025-02563-7","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:pure.atira.dk:publications/2aba1cb9-308f-4b07-bf5a-94f4f7917720","is_oa":true,"landing_page_url":"https://pure.au.dk/portal/en/publications/2aba1cb9-308f-4b07-bf5a-94f4f7917720","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Quercia, A, Nader, F, Morrison, A, Scharr, H & Assent, I 2025, 'Focal Sampling : SGD biased towards early important samples for efficient image classification with augmentation selection', Knowledge and Information Systems, vol. 67, no. 11, pp. 11161-11191. https://doi.org/10.1007/s10115-025-02563-7","raw_type":"info:eu-repo/semantics/article"},{"id":"doi:10.18154/rwth-2026-01092","is_oa":true,"landing_page_url":"https://doi.org/10.18154/rwth-2026-01092","pdf_url":null,"source":{"id":"https://openalex.org/S4306401033","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.1007/s10115-025-02563-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10115-025-02563-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10115-025-02563-7.pdf","source":{"id":"https://openalex.org/S81770430","display_name":"Knowledge and Information Systems","issn_l":"0219-1377","issn":["0219-1377","0219-3116"],"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":"Knowledge and Information Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320321752","display_name":"Forschungszentrum J\u00fclich","ror":"https://ror.org/02nv7yv05"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413791360.pdf","grobid_xml":"https://content.openalex.org/works/W4413791360.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2147800946","https://openalex.org/W2148143831","https://openalex.org/W2296073425","https://openalex.org/W2949736877","https://openalex.org/W2962971773","https://openalex.org/W2963351448","https://openalex.org/W2963516811","https://openalex.org/W3035261884","https://openalex.org/W3035682985","https://openalex.org/W3113682753","https://openalex.org/W3182707920","https://openalex.org/W3198227882","https://openalex.org/W3202897123","https://openalex.org/W3210989681","https://openalex.org/W4386065512","https://openalex.org/W4391528874"],"related_works":["https://openalex.org/W618248309","https://openalex.org/W2377336366","https://openalex.org/W1568097102","https://openalex.org/W4390419160","https://openalex.org/W1601203902","https://openalex.org/W2075798043","https://openalex.org/W2102464536","https://openalex.org/W4225671779","https://openalex.org/W2361332776","https://openalex.org/W2248934910"],"abstract_inverted_index":{"Abstract":[0],"In":[1,71,102],"deep":[2],"learning,":[3],"using":[4,92,131],"larger":[5],"training":[6,26,58,91],"datasets":[7,124,148],"usually":[8],"leads":[9],"to":[10,51,73,81],"more":[11,17,30,53,63],"accurate":[12],"models.":[13],"However,":[14],"simply":[15],"adding":[16],"but":[18],"redundant":[19],"data":[20,133],"may":[21,28],"be":[22,29,52],"inefficient,":[23],"as":[24,85],"some":[25],"samples":[27,47],"informative":[31],"than":[32,114,154],"others.":[33],"We":[34],"propose":[35],"Focal":[36],"Sampling,":[37],"a":[38,56],"method":[39],"that":[40,48,108,138,146],"biases":[41],"SGD":[42],"(Stochastic":[43],"Gradient":[44],"Descent)":[45],"towards":[46],"are":[49,125],"found":[50],"important":[54],"after":[55],"few":[57],"epochs,":[59],"by":[60,155],"sampling":[61],"them":[62],"often":[64],"for":[65],"the":[66,69,93,103],"rest":[67],"of":[68,157],"training.":[70,101],"contrast":[72],"state-of-the-art,":[74],"our":[75,109,139],"approach":[76,140],"requires":[77],"less":[78],"computational":[79],"overhead":[80],"estimate":[82],"sample":[83],"importance,":[84,152],"it":[86],"computes":[87],"estimates":[88],"once":[89],"during":[90],"prediction":[94],"probabilities,":[95],"and":[96,116,145],"does":[97],"not":[98,126],"require":[99],"restarting":[100],"experimental":[104],"evaluation,":[105],"we":[106],"see":[107],"learning":[110],"technique":[111],"trains":[112],"faster":[113],"state-of-the-art":[115],"can":[117,159],"achieve":[118,160],"higher":[119,161],"test":[120,162],"accuracy,":[121],"especially":[122],"when":[123,130],"well":[127],"balanced":[128],"or":[129],"multiple":[132],"augmentations.":[134],"Lastly,":[135],"results":[136],"suggest":[137],"has":[141],"intrinsic":[142],"balancing":[143,147],"properties":[144],"based":[149],"on":[150],"class":[151],"rather":[153],"number":[156],"samples,":[158],"accuracy.":[163],"Code":[164],"is":[165],"available":[166],"at":[167],"https://jugit.fz-juelich.de/ias-8/sgd_biased":[168],".":[169]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}