{"id":"https://openalex.org/W7160636189","doi":"https://doi.org/10.48550/arxiv.2605.05331","title":"ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters","display_name":"ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160636189","doi":"https://doi.org/10.48550/arxiv.2605.05331"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.05331","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05331","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.05331","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045296702","display_name":"Philippe Hansen-Estruch","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hansen-Estruch, Philippe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135691322","display_name":"Jiahui Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jiahui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060895155","display_name":"Vivek Ramanujan","orcid":"https://orcid.org/0000-0002-3247-5575"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramanujan, Vivek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135695658","display_name":"Orr Zohar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zohar, Orr","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135674786","display_name":"Yan Ping","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ping, Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135343347","display_name":"Animesh Sinha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sinha, Animesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074117139","display_name":"Markos Georgopoulos","orcid":"https://orcid.org/0000-0001-5928-515X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Georgopoulos, Markos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111340037","display_name":"Edgar Schoenfeld","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schoenfeld, Edgar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135639586","display_name":"Ji Hou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hou, Ji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135639166","display_name":"Felix Juefei-Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Juefei-Xu, Felix","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088120102","display_name":"Sriram Vishwanath","orcid":"https://orcid.org/0000-0003-3112-4885"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vishwanath, Sriram","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135642044","display_name":"Ali Thabet","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thabet, Ali","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":0.42399999499320984,"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":0.42399999499320984,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.3887999951839447,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.11069999635219574,"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/autoencoder","display_name":"Autoencoder","score":0.7817000150680542},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.6880999803543091},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.48570001125335693},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.45329999923706055},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.448199987411499},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38089999556541443},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.3765999972820282},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3659999966621399},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.3513999879360199}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7817000150680542},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.6880999803543091},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6546000242233276},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.569599986076355},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.48570001125335693},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.45329999923706055},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.448199987411499},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3659999966621399},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3513999879360199},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34929999709129333},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.33390000462532043},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.33149999380111694},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32330000400543213},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.31769999861717224},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2614000141620636},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.25690001249313354},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.05331","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05331","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.05331","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05331","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":{"Vision":[0],"Transformer":[1],"(ViT)":[2],"autoencoders":[3],"have":[4],"emerged":[5],"as":[6,25],"compelling":[7],"tokenizers":[8,20],"for":[9,86,106],"images,":[10],"offering":[11],"improved":[12],"reconstruction":[13,66,135],"over":[14],"convolutional":[15],"tokenizers.":[16,72],"However,":[17],"existing":[18],"ViT":[19],"cannot":[21],"explore":[22],"this":[23,169],"landscape":[24],"performance":[26],"degrades":[27],"outside":[28],"training":[29,108],"resolutions,":[30],"and":[31,90,93,103,119,138,144,161],"reliance":[32],"on":[33,115],"adversarial":[34],"losses":[35],"prevents":[36],"stable":[37,107],"scaling.":[38],"ViTok":[39],"(Hansen-Estruch":[40],"et":[41],"al.,":[42],"2025)":[43],"found":[44],"that":[45,99,156],"the":[46,124,159,162,165],"compression":[47],"ratio":[48],"r":[49,56],"mediates":[50],"a":[51,94],"reconstruction-generation":[52],"trade-off":[53],"where":[54],"lower":[55],"means":[57],"better":[58],"reconstructions":[59],"but":[60],"harder":[61],"generations,":[62],"so":[63],"improving":[64],"tokenizer":[65],"is":[67,113],"key":[68],"to":[69,121,128],"more":[70],"Pareto-optimal":[71],"We":[73],"introduce":[74],"ViTok-v2,":[75],"which":[76],"addresses":[77],"these":[78],"limitations":[79],"with":[80,150],"native":[81],"resolution":[82],"support":[83],"via":[84],"NaFlex":[85],"generalization":[87],"across":[88],"resolutions":[89],"aspect":[91],"ratios,":[92],"novel":[95],"DINOv3":[96],"perceptual":[97],"loss":[98],"replaces":[100],"both":[101,158],"LPIPS":[102],"GAN":[104],"objectives":[105],"at":[109,136,142],"any":[110],"scale.":[111],"ViTok-v2":[112,130],"trained":[114],"about":[116],"2B":[117],"images":[118],"scaled":[120],"5B":[122],"parameters,":[123],"largest":[125],"image":[126],"autoencoder":[127,160],"date.":[129],"matches":[131],"or":[132],"exceeds":[133],"state-of-the-art":[134],"256p":[137],"outperforms":[139],"all":[140],"baselines":[141],"512p":[143],"above.":[145],"In":[146],"joint":[147],"scaling":[148,157],"experiments":[149],"flow":[151],"matching":[152],"generators,":[153],"we":[154],"show":[155],"generator":[163],"advances":[164],"Pareto":[166],"frontier":[167],"of":[168],"trade-off.":[170]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}