{"id":"https://openalex.org/W4386521159","doi":"https://doi.org/10.1145/3587716.3587780","title":"Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization","display_name":"Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization","publication_year":2023,"publication_date":"2023-02-17","ids":{"openalex":"https://openalex.org/W4386521159","doi":"https://doi.org/10.1145/3587716.3587780"},"language":"en","primary_location":{"id":"doi:10.1145/3587716.3587780","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3587716.3587780","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3587716.3587780?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 15th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3587716.3587780?download=true","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053236067","display_name":"Christoph Linse","orcid":"https://orcid.org/0000-0002-7039-5189"},"institutions":[{"id":"https://openalex.org/I9341345","display_name":"University of L\u00fcbeck","ror":"https://ror.org/00t3r8h32","country_code":"DE","type":"education","lineage":["https://openalex.org/I9341345"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Christoph Linse","raw_affiliation_strings":["Institute for Neuro- and Bioinformatics, University of L\u00fcbeck, Germany"],"raw_orcid":"https://orcid.org/0000-0002-7039-5189","affiliations":[{"raw_affiliation_string":"Institute for Neuro- and Bioinformatics, University of L\u00fcbeck, Germany","institution_ids":["https://openalex.org/I9341345"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014856404","display_name":"Thomas Martinetz","orcid":"https://orcid.org/0000-0002-4539-4475"},"institutions":[{"id":"https://openalex.org/I9341345","display_name":"University of L\u00fcbeck","ror":"https://ror.org/00t3r8h32","country_code":"DE","type":"education","lineage":["https://openalex.org/I9341345"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Thomas Martinetz","raw_affiliation_strings":["Institute for Neuro- and Bioinformatics, University of L\u00fcbeck, Germany"],"raw_orcid":"https://orcid.org/0000-0002-4539-4475","affiliations":[{"raw_affiliation_string":"Institute for Neuro- and Bioinformatics, University of L\u00fcbeck, Germany","institution_ids":["https://openalex.org/I9341345"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5053236067"],"corresponding_institution_ids":["https://openalex.org/I9341345"],"apc_list":null,"apc_paid":null,"fwci":0.852,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.78881079,"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":"279","last_page":"283"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","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/T12676","display_name":"Machine Learning and ELM","score":0.9966999888420105,"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":0.9958000183105469,"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/scratch","display_name":"Scratch","score":0.7966353893280029},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7420204281806946},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5960894823074341},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5823611617088318},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49636250734329224},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3882777690887451},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.08850923180580139}],"concepts":[{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.7966353893280029},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7420204281806946},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5960894823074341},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5823611617088318},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49636250734329224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3882777690887451},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.08850923180580139}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3587716.3587780","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3587716.3587780","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3587716.3587780?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 15th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3587716.3587780","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3587716.3587780","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3587716.3587780?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 15th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386521159.pdf"},"referenced_works_count":8,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W2108598243","https://openalex.org/W2138011018","https://openalex.org/W2155904486","https://openalex.org/W2165828254","https://openalex.org/W2194775991","https://openalex.org/W2533598788","https://openalex.org/W2963518130"],"related_works":["https://openalex.org/W4281658507","https://openalex.org/W4224998860","https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694"],"abstract_inverted_index":{"Recent":[0],"findings":[1,49],"have":[2],"shown":[3],"that":[4,59,129],"highly":[5],"over-parameterized":[6],"Neural":[7,63],"Networks":[8,64],"generalize":[9],"without":[10,79],"pretraining":[11],"or":[12,84],"explicit":[13,82],"regularization.":[14],"It":[15],"is":[16,31,35],"achieved":[17],"with":[18,65,71,107,134,144],"zero":[19],"training":[20,28,76,152],"error,":[21],"i.e.,":[22],"complete":[23],"over-fitting":[24],"by":[25],"memorizing":[26],"the":[27,51,88,97,121],"data.":[29],"This":[30],"surprising,":[32],"since":[33],"it":[34],"completely":[36],"against":[37],"traditional":[38],"machine":[39],"learning":[40],"wisdom.":[41],"In":[42],"our":[43],"empirical":[44],"study":[45,116],"we":[46,127],"fortify":[47],"these":[48],"in":[50],"domain":[52],"of":[53,67,75,96,124],"fine-grained":[54],"image":[55,80],"classification.":[56],"We":[57,86],"show":[58,128],"very":[60],"large":[61],"Convolutional":[62],"millions":[66],"weights":[68,137],"do":[69],"learn":[70],"only":[72,150],"a":[73,113,130],"handful":[74],"samples":[77,153],"and":[78,92,105,110,117,142],"augmentation,":[81],"regularization":[83],"pretraining.":[85],"train":[87],"architectures":[89],"ResNet018,":[90],"ResNet101":[91],"VGG19":[93,133],"on":[94],"subsets":[95],"difficult":[98],"benchmark":[99],"datasets":[100],"Caltech101,":[101],"CUB_200_2011,":[102],"FGVCAircraft,":[103],"Flowers102":[104],"StanfordCars":[106],"100":[108],"classes":[109],"more,":[111],"perform":[112],"comprehensive":[114],"comparative":[115],"draw":[118],"implications":[119],"for":[120],"practical":[122],"application":[123],"CNNs.":[125],"Finally,":[126],"randomly":[131],"initialized":[132],"140":[135],"million":[136],"learns":[138],"to":[139,146],"distinguish":[140],"airplanes":[141],"motorbikes":[143],"up":[145],"95%":[147],"accuracy":[148],"using":[149],"20":[151],"per":[154],"class.":[155]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-11T06:11:40.159057","created_date":"2025-10-10T00:00:00"}
