{"id":"https://openalex.org/W7167231315","doi":"https://doi.org/10.48550/arxiv.2607.02508","title":"From SRA to Self-Flow: Data Augmentation or Self-Supervision?","display_name":"From SRA to Self-Flow: Data Augmentation or Self-Supervision?","publication_year":2026,"publication_date":"2026-07-02","ids":{"openalex":"https://openalex.org/W7167231315","doi":"https://doi.org/10.48550/arxiv.2607.02508"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.02508","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.02508","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,"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.02508","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139979818","display_name":"Dengyang Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Dengyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078914856","display_name":"M Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Mengmeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139994514","display_name":"Harry Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Harry","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140000411","display_name":"Jingdong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jingdong","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.3720000088214874,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.3720000088214874,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.07819999754428864,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.05260000005364418,"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/noise","display_name":"Noise (video)","score":0.5655999779701233},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5501999855041504},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4875999987125397},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.46549999713897705},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4113999903202057},{"id":"https://openalex.org/keywords/performance-improvement","display_name":"Performance improvement","score":0.4000000059604645},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.39559999108314514},{"id":"https://openalex.org/keywords/ask-price","display_name":"Ask price","score":0.38929998874664307}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7196999788284302},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5655999779701233},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5501999855041504},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4875999987125397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4666000008583069},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.46549999713897705},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4113999903202057},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.4000000059604645},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.39559999108314514},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.38929998874664307},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3601999878883362},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33079999685287476},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C144745244","wikidata":"https://www.wikidata.org/wiki/Q4927286","display_name":"Blocking (statistics)","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C9083635","wikidata":"https://www.wikidata.org/wiki/Q2133535","display_name":"Noise shaping","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.26440000534057617},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.02508","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.02508","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,"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":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.02508","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.02508","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,"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Representation":[0],"alignment":[1,34,178],"has":[2],"become":[3],"an":[4,153],"effective":[5,163],"way":[6],"to":[7,48,58,116,139,166],"accelerate":[8],"diffusion":[9,37],"transformer":[10],"training":[11,164,169],"and":[12,22,80,128,181,184],"improve":[13,131],"generation":[14],"quality.":[15],"Recent":[16],"self-alignment":[17],"methods,":[18],"such":[19,122],"as":[20,108],"SRA":[21,47,138],"Self-Flow,":[23,49],"further":[24],"remove":[25],"the":[26,36,41,44,83,91,104,135,168,186],"dependency":[27],"on":[28,172,191],"external":[29],"pretrained":[30],"encoders":[31],"by":[32,156],"constructing":[33],"within":[35],"model":[38],"itself.":[39],"However,":[40],"mechanism":[42],"behind":[43],"improvement":[45,136],"from":[46,87,137,143],"dual-time":[50],"scheduling,":[51],"remains":[52],"under-examined:":[53],"Self-Flow":[54,109,140],"attributes":[55],"its":[56],"gain":[57,84],"interactions":[59],"between":[60,113],"tokens":[61,68,114],"at":[62],"different":[63,117],"noise":[64,92,118],"levels,":[65],"where":[66],"cleaner":[67],"help":[69],"infer":[70],"noisier":[71],"ones.":[72],"In":[73],"this":[74,78,189],"work,":[75],"we":[76,98,175],"revisit":[77],"explanation":[79],"ask":[81],"whether":[82],"instead":[85],"comes":[86,142],"data":[88,144],"augmentation":[89,154],"along":[90],"dimension.":[93],"To":[94],"disentangle":[95],"these":[96,173],"factors,":[97],"introduce":[99],"Attention":[100,149],"Separation,":[101],"which":[102],"preserves":[103],"same":[105],"dual-timestep":[106,180],"input":[107],"while":[110],"blocking":[111],"attention":[112],"assigned":[115],"levels.":[119],"Surprisingly,":[120],"removing":[121],"interaction":[123],"does":[124],"not":[125],"degrade":[126],"performance":[127],"can":[129],"even":[130],"it,":[132],"suggesting":[133],"that":[134,148],"mainly":[141],"augmentation.":[145],"Furthermore,We":[146],"show":[147],"Separation":[150],"itself":[151],"provides":[152],"effect":[155],"splitting":[157],"a":[158],"single":[159],"image":[160],"into":[161],"multiple":[162],"parts":[165],"expand":[167],"data.":[170],"Based":[171],"observations,":[174],"combine":[176],"self-representation":[177],"with":[179],"attention-separation":[182],"augmentation,":[183],"demonstrate":[185],"effectiveness":[187],"of":[188],"design":[190],"ImageNet.":[192]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-04T00:00:00"}
