{"id":"https://openalex.org/W7164015409","doi":"https://doi.org/10.48550/arxiv.2606.08788","title":"MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training","display_name":"MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training","publication_year":2026,"publication_date":"2026-06-07","ids":{"openalex":"https://openalex.org/W7164015409","doi":"https://doi.org/10.48550/arxiv.2606.08788"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.08788","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08788","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.08788","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102685536","display_name":"Lianyu Pang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pang, Lianyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128068063","display_name":"Tianlin Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Tianlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138206007","display_name":"Cheng Da","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Da, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013651586","display_name":"Changqian Yu","orcid":"https://orcid.org/0000-0002-4488-4157"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Changqian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138267800","display_name":"Huan Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Huan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138233083","display_name":"Kun Gai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gai, Kun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138252113","display_name":"Song Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Song","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138222770","display_name":"Wenhan Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Wenhan","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":0.37369999289512634,"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":0.37369999289512634,"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/T11338","display_name":"Advancements in Photolithography Techniques","score":0.11060000211000443,"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/T11019","display_name":"Image Enhancement Techniques","score":0.08869999647140503,"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/security-token","display_name":"Security token","score":0.8184000253677368},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6887000203132629},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5508999824523926},{"id":"https://openalex.org/keywords/usable","display_name":"USable","score":0.5453000068664551},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4699000120162964},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.41819998621940613}],"concepts":[{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.8184000253677368},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6931999921798706},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6887000203132629},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5508999824523926},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.5453000068664551},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4699000120162964},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4259999990463257},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.41819998621940613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4066999852657318},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37119999527931213},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C29406490","wikidata":"https://www.wikidata.org/wiki/Q1420659","display_name":"Fisher information","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2827000021934509},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2603999972343445}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.08788","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08788","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.08788","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08788","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":[1,37,95,127,131,155,163],"with":[2,21,83],"pretrained":[3],"vision":[4,26],"models":[5,44],"has":[6],"recently":[7],"shown":[8],"strong":[9],"potential":[10],"for":[11],"accelerating":[12],"diffusion":[13,19,43],"transformer":[14],"training.":[15,138],"By":[16,139],"aligning":[17],"intermediate":[18],"features":[20,58],"clean-image":[22,115],"representations":[23],"from":[24,61,71],"self-supervised":[25],"encoders,":[27],"existing":[28],"methods":[29],"improve":[30],"convergence":[31],"and":[32,103,161],"generation":[33],"quality.":[34],"However,":[35],"such":[36],"also":[38],"introduces":[39],"a":[40,72,88,124,185],"non-trivial":[41],"constraint:":[42],"operate":[45],"on":[46,110,156],"noisy":[47],"inputs":[48],"whose":[49],"usable":[50],"information":[51,175,193],"varies":[52],"across":[53,147,194],"timesteps,":[54],"while":[55],"the":[56,94,106,111,141,151,157,174],"reference":[57],"are":[59],"extracted":[60],"clean":[62],"images.":[63],"In":[64],"this":[65,69,119],"paper,":[66],"we":[67,121,182],"revisit":[68],"mismatch":[70],"token-level":[73],"perspective.":[74],"We":[75],"find":[76],"that,":[77],"under":[78,169],"full-token":[79],"representation":[80,126,154],"alignment,":[81],"tokens":[82,101,195],"large":[84],"alignment-gradient":[85],"norms":[86],"exhibit":[87],"stable":[89,168],"spatial":[90],"preference,":[91],"suggesting":[92],"that":[93,129,165,191],"objective":[96],"does":[97],"not":[98],"affect":[99],"all":[100],"uniformly":[102],"may":[104],"encourage":[105],"model":[107,142],"to":[108,132,143],"rely":[109],"complete":[112,158],"set":[113,160],"of":[114,153],"tokens.":[116],"To":[117,172],"address":[118],"issue,":[120],"propose":[122],"MaskAlign,":[123],"token-subset":[125,170],"method":[128],"applies":[130],"randomly":[133],"sampled":[134],"token":[135,145,159,188],"subsets":[136,146],"during":[137],"exposing":[140],"different":[144],"iterations,":[148],"MaskAlign":[149],"reduces":[150],"dependence":[152],"encourages":[162],"behavior":[164],"is":[166],"more":[167],"perturbations.":[171],"mitigate":[173],"loss":[176],"caused":[177],"by":[178],"directly":[179],"dropping":[180],"tokens,":[181],"further":[183],"introduce":[184],"lightweight":[186],"pre-mask":[187],"mixing":[189],"block":[190],"shares":[192],"before":[196],"masking.":[197]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-10T00:00:00"}
