{"id":"https://openalex.org/W2920796904","doi":"https://doi.org/10.5220/0007456306910698","title":"Measuring the Data Efficiency of Deep Learning Methods","display_name":"Measuring the Data Efficiency of Deep Learning Methods","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2920796904","doi":"https://doi.org/10.5220/0007456306910698","mag":"2920796904"},"language":"en","primary_location":{"id":"doi:10.5220/0007456306910698","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0007456306910698","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5220/0007456306910698","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Hlynur Hlynsson","orcid":null},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hlynur Hlynsson","raw_affiliation_strings":["Ruhr University Bochum, Universit\u00e4tsstra\u00dfe 150, 44801 Bochum and Germany, --- Select a Country ---"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruhr University Bochum, Universit\u00e4tsstra\u00dfe 150, 44801 Bochum and Germany, --- Select a Country ---","institution_ids":["https://openalex.org/I904495901"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Alberto Escalante-B.","orcid":null},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alberto Escalante-B.","raw_affiliation_strings":["Ruhr University Bochum, Universit\u00e4tsstra\u00dfe 150, 44801 Bochum and Germany, --- Select a Country ---"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruhr University Bochum, Universit\u00e4tsstra\u00dfe 150, 44801 Bochum and Germany, --- Select a Country ---","institution_ids":["https://openalex.org/I904495901"]}]},{"author_position":"last","author":{"id":null,"display_name":"Laurenz Wiskott","orcid":null},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Laurenz Wiskott","raw_affiliation_strings":["Ruhr University Bochum, Universit\u00e4tsstra\u00dfe 150, 44801 Bochum and Germany, --- Select a Country ---"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruhr University Bochum, Universit\u00e4tsstra\u00dfe 150, 44801 Bochum and Germany, --- Select a Country ---","institution_ids":["https://openalex.org/I904495901"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I904495901"],"apc_list":null,"apc_paid":null,"fwci":0.2195,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.48779834,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"691","last_page":"698"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9991999864578247,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9991000294685364,"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/mnist-database","display_name":"MNIST database","score":0.9386000037193298},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7623000144958496},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6654000282287598},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6431000232696533},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5202000141143799},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5131000280380249},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.47099998593330383},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.46470001339912415}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.9386000037193298},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7847999930381775},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7623000144958496},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7193999886512756},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6654000282287598},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6431000232696533},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5202000141143799},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5131000280380249},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5108000040054321},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.47099998593330383},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.46470001339912415},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4544999897480011},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4350000023841858},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.39899998903274536},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.35589998960494995},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3499000072479248},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.2533999979496002}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5220/0007456306910698","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0007456306910698","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1907.02549","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.02549","pdf_url":"https://arxiv.org/pdf/1907.02549","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.5220/0007456306910698","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0007456306910698","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,86],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,20],"new":[6],"experimental":[7],"protocol":[8],"and":[9,36,48,62,78],"use":[10],"it":[11],"to":[12],"benchmark":[13],"the":[14,60,72,89],"data":[15,64],"efficiency":[16],"---":[17,26],"performance":[18],"as":[19],"function":[21],"of":[22,27,59],"training":[23],"set":[24],"size":[25],"two":[28],"deep":[29],"learning":[30,50,105],"algorithms,":[31],"convolutional":[32],"neural":[33],"networks":[34,70],"(CNNs)":[35],"hierarchical":[37],"information-preserving":[38],"graph-based":[39],"slow":[40],"feature":[41],"analysis":[42],"(HiGSFA),":[43],"for":[44,83],"tasks":[45],"in":[46],"classification":[47],"transfer":[49],"scenarios.":[51],"The":[52,93],"algorithms":[53],"are":[54,74,98],"trained":[55,75],"on":[56,76],"different-sized":[57],"subsets":[58],"MNIST":[61,84],"Omniglot":[63],"sets.":[65],"HiGSFA":[66],"outperforms":[67],"standard":[68],"CNN":[69],"when":[71],"models":[73],"50":[77],"200":[79],"samples":[80],"per":[81],"class":[82],"classification.":[85],"other":[87],"cases,":[88],"CNNs":[90],"perform":[91],"better.":[92],"results":[94],"suggest":[95],"that":[96],"there":[97],"cases":[99],"where":[100],"greedy,":[101],"locally":[102],"optimal":[103],"bottom-up":[104],"is":[106],"equally":[107],"or":[108],"more":[109],"powerful":[110],"than":[111],"global":[112],"gradient-based":[113],"learning.":[114]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2019-03-22T00:00:00"}
