{"id":"https://openalex.org/W7204882947","doi":"https://doi.org/10.48550/arxiv.2608.30594","title":"Learning Radio Astronomical Representations with LeJEPA and Very Small Models","display_name":"Learning Radio Astronomical Representations with LeJEPA and Very Small Models","publication_year":2026,"publication_date":"2026-08-31","ids":{"openalex":"https://openalex.org/W7204882947","doi":"https://doi.org/10.48550/arxiv.2608.30594"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.30594","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.30594","pdf_url":"https://arxiv.org/pdf/2608.30594","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,"listed_in":[],"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"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.30594","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060659852","display_name":"Erica Lastufka","orcid":"https://orcid.org/0000-0003-1894-2074","observed_orcids":["https://orcid.org/0000-0003-1894-2074"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lastufka, Erica","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045768127","display_name":"Mariia Drozdova","orcid":"https://orcid.org/0000-0001-6575-4290","observed_orcids":["https://orcid.org/0000-0001-6575-4290"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Drozdova, Mariia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083279037","display_name":"Vitaliy Kinakh","orcid":"https://orcid.org/0000-0001-5301-9141","observed_orcids":["https://orcid.org/0000-0001-5301-9141"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kinakh, Vitaliy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030544448","display_name":"Taras Holotyak","orcid":"https://orcid.org/0009-0002-2490-0276","observed_orcids":["https://orcid.org/0009-0002-2490-0276"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Holotyak, Taras","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051029980","display_name":"Miroslava Dessauges-Zavadsky","orcid":"https://orcid.org/0000-0003-0348-2917","observed_orcids":["https://orcid.org/0000-0003-0348-2917"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dessuages-Zavadsky, Miroslava","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062010375","display_name":"D. Schaerer","orcid":"https://orcid.org/0000-0001-7144-7182","observed_orcids":["https://orcid.org/0000-0001-7144-7182"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schaerer, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138146109","display_name":"Svyatoslav Voloshynovskiy","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Voloshynovskiy, Svyatoslav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.6996999979019165,"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":0.6996999979019165,"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/T12450","display_name":"Radio Astronomy Observations and Technology","score":0.09790000319480896,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.06430000066757202,"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/self-supervised-pretraining","display_name":"self-supervised pre-training","score":0.781000018119812},{"id":"https://openalex.org/keywords/latent-space-regularization","display_name":"latent space regularization","score":0.7200000286102295},{"id":"https://openalex.org/keywords/parameter-efficient-fine-tuning","display_name":"parameter-efficient fine-tuning","score":0.5590000152587891}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6029000282287598},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6000000238418579},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5831999778747559},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.5601999759674072},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5430999994277954},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5227000117301941},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3869999945163727},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38420000672340393},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C98444146","wikidata":"https://www.wikidata.org/wiki/Q318","display_name":"Galaxy","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C138379479","wikidata":"https://www.wikidata.org/wiki/Q1116876","display_name":"Scientific modelling","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.30594","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.30594","pdf_url":"https://arxiv.org/pdf/2608.30594","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,"listed_in":[],"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"},{"id":"doi:10.48550/arxiv.2608.30594","doi":"https://doi.org/10.48550/arxiv.2608.30594","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.30594","pdf_url":null,"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,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.30594","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.30594","pdf_url":"https://arxiv.org/pdf/2608.30594","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,"listed_in":[],"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"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4390135262","https://openalex.org/W7164181382","https://openalex.org/W4410742935","https://openalex.org/W7111843150","https://openalex.org/W4416545270","https://openalex.org/W4417408859","https://openalex.org/W7172428533","https://openalex.org/W7166711578","https://openalex.org/W7128471126","https://openalex.org/W4402387332"],"abstract_inverted_index":{"Representations":[0],"learned":[1],"by":[2],"vision":[3,71],"foundation":[4,111,149],"models":[5,72,139,150],"pretrained":[6,75],"on":[7,21,76],"natural":[8,55],"images":[9],"have":[10],"been":[11],"shown":[12],"to":[13,46,64,93,107,140],"be":[14],"useful":[15],"for":[16,135],"out-of-domain":[17],"astronomical":[18,51],"images.":[19],"Performance":[20],"scientific":[22,152],"downstream":[23],"tasks":[24],"increases":[25],"with":[26,82,144],"model":[27,112],"size,":[28],"which":[29],"both":[30],"carries":[31],"higher":[32],"inference":[33],"costs":[34],"and":[35,120],"limits":[36],"scalability,":[37],"even":[38],"when":[39],"considering":[40],"parameter-efficient":[41],"adaptation.":[42],"An":[43],"alternative":[44],"is":[45,133],"learn":[47,65],"representations":[48,67,117,145],"directly":[49],"from":[50,147],"observations":[52],"rather":[53],"than":[54],"images,":[56,80],"through":[57],"self-supervised":[58,84],"pretraining.":[59],"We":[60,86],"evaluate":[61],"LeJEPA's":[62,89],"ability":[63],"robust":[66],"using":[68],"very":[69],"small":[70,137],"($\\sim$6M":[73],"parameters)":[74],"Radio":[77],"Galaxy":[78],"Zoo":[79],"comparing":[81],"established":[83],"frameworks.":[85],"test":[87],"whether":[88],"latent-space":[90],"regularization":[91],"leads":[92],"better":[94],"radio":[95],"galaxy":[96],"morphology":[97],"classification.":[98],"Across":[99],"three":[100],"evaluation":[101,121],"datasets,":[102],"LeJEPA":[103],"achieves":[104],"performance":[105,142],"comparable":[106],"a":[108],"substantially":[109],"larger":[110],"while":[113],"producing":[114],"more":[115],"consistent":[116],"across":[118],"training":[119],"datasets.":[122],"These":[123],"results":[124],"suggest":[125],"that":[126],"the":[127],"choice":[128],"of":[129],"representation":[130],"learning":[131],"objective":[132],"critical":[134],"enabling":[136],"domain-specific":[138],"achieve":[141],"competitive":[143],"transferred":[146],"large":[148],"in":[151],"imaging.":[153]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-09-02T00:00:00"}
