{"id":"https://openalex.org/W7154502741","doi":"https://doi.org/10.48550/arxiv.2604.12805","title":"Image-to-Image Translation Framework Embedded with Rotation Symmetry Priors","display_name":"Image-to-Image Translation Framework Embedded with Rotation Symmetry Priors","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154502741","doi":"https://doi.org/10.48550/arxiv.2604.12805"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.12805","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12805","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.2604.12805","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133643724","display_name":"Feiyu Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Feiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101882968","display_name":"Heran Yang","orcid":"https://orcid.org/0000-0002-3430-4761"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Heran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133651282","display_name":"Qihong Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Qihong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133689578","display_name":"Kai Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128781252","display_name":"Qi Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Qi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133648470","display_name":"Deyu Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Deyu","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":0.8222000002861023,"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":0.8222000002861023,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.046799998730421066,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.03720000013709068,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/equivariant-map","display_name":"Equivariant map","score":0.8636999726295471},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.7968999743461609},{"id":"https://openalex.org/keywords/rotation","display_name":"Rotation (mathematics)","score":0.7006000280380249},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.6678000092506409},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.6396999955177307},{"id":"https://openalex.org/keywords/symmetry","display_name":"Symmetry (geometry)","score":0.5782999992370605},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5246000289916992},{"id":"https://openalex.org/keywords/image-translation","display_name":"Image translation","score":0.5123000144958496},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43560001254081726}],"concepts":[{"id":"https://openalex.org/C171036898","wikidata":"https://www.wikidata.org/wiki/Q256355","display_name":"Equivariant map","level":2,"score":0.8636999726295471},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7968999743461609},{"id":"https://openalex.org/C74050887","wikidata":"https://www.wikidata.org/wiki/Q848368","display_name":"Rotation (mathematics)","level":2,"score":0.7006000280380249},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.6678000092506409},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.6396999955177307},{"id":"https://openalex.org/C2779886137","wikidata":"https://www.wikidata.org/wiki/Q21030012","display_name":"Symmetry (geometry)","level":2,"score":0.5782999992370605},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5282999873161316},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5246000289916992},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5175999999046326},{"id":"https://openalex.org/C2779757391","wikidata":"https://www.wikidata.org/wiki/Q6002292","display_name":"Image translation","level":3,"score":0.5123000144958496},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5022000074386597},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C49766605","wikidata":"https://www.wikidata.org/wiki/Q207643","display_name":"Linear map","level":2,"score":0.398499995470047},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.391400009393692},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3856000006198883},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3352000117301941},{"id":"https://openalex.org/C126795593","wikidata":"https://www.wikidata.org/wiki/Q7333813","display_name":"Rigid transformation","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C84229","wikidata":"https://www.wikidata.org/wiki/Q2116709","display_name":"Translational symmetry","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.29829999804496765},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27160000801086426},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C136119220","wikidata":"https://www.wikidata.org/wiki/Q1000660","display_name":"Algebra over a field","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.25189998745918274},{"id":"https://openalex.org/C44306375","wikidata":"https://www.wikidata.org/wiki/Q902019","display_name":"Symmetry group","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.12805","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12805","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.2604.12805","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12805","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Image-to-image":[0],"translation":[1,72],"(I2I)":[2],"is":[3,223],"a":[4,17,21,25,87,127,139,160,180,192],"fundamental":[5],"task":[6],"in":[7,24,175,185,213],"computer":[8],"vision,":[9],"focused":[10],"on":[11,130,134],"mapping":[12],"an":[13],"input":[14],"image":[15,23,131],"from":[16],"source":[18],"domain":[19,27],"to":[20,81,90],"corresponding":[22],"target":[26],"while":[28],"preserving":[29],"domain-invariant":[30,114],"features":[31],"and":[32,50,113,118,137,178,201,217],"adapting":[33],"domain-specific":[34],"attributes.":[35],"Despite":[36],"the":[37,45,63,91,103,110,122,164,183,199,208],"remarkable":[38],"success":[39],"of":[40,47,93,105,109,116,163,167,194,204,210],"deep":[41],"learning-based":[42],"I2I":[43,85,195],"approaches,":[44],"lack":[46],"paired":[48],"data":[49],"unsupervised":[51],"learning":[52],"framework":[53],"still":[54],"hinder":[55],"their":[56],"effectiveness.":[57],"In":[58],"this":[59,97],"work,":[60],"we":[61,75,125,197],"address":[62],"challenge":[64],"by":[65],"incorporating":[66],"transformation":[67,141,149],"symmetry":[68,132,152],"priors":[69,133],"into":[70],"image-to-image":[71],"networks.":[73],"Specifically,":[74],"introduce":[76],"rotation":[77,83,106],"group":[78],"equivariant":[79,84,143,211],"convolutions":[80,144],"achieve":[82],"framework,":[86],"novel":[88,140],"contribution,":[89],"best":[92],"our":[94,205],"knowledge,":[95],"along":[96],"research":[98],"direction.":[99],"This":[100],"design":[101],"ensures":[102],"preservation":[104,153],"symmetry,":[107],"one":[108],"most":[111],"intrinsic":[112],"properties":[115],"natural":[117],"scientific":[119],"images,":[120],"throughout":[121],"network.":[123],"Furthermore,":[124],"conduct":[126],"systematic":[128],"study":[129],"real":[135],"dataset":[136],"propose":[138],"learnable":[142],"(TL-Conv)":[145],"that":[146,170],"adaptively":[147],"learns":[148],"groups,":[150],"enhancing":[151,214],"across":[154,191],"diverse":[155],"datasets.":[156],"We":[157],"also":[158],"provide":[159,179],"theoretical":[161],"analysis":[162],"equivariance":[165,174],"error":[166,184],"TL-Conv,":[168],"proving":[169],"it":[171],"maintains":[172],"exact":[173],"continuous":[176],"domains":[177],"bound":[181],"for":[182],"discrete":[186],"cases.":[187],"Through":[188],"extensive":[189],"experiments":[190],"range":[193],"tasks,":[196],"validate":[198],"effectiveness":[200],"superior":[202],"performance":[203],"approach,":[206],"highlighting":[207],"potential":[209],"networks":[212],"generation":[215],"quality":[216],"its":[218],"broad":[219],"applicability.":[220],"Our":[221],"code":[222],"available":[224],"at":[225],"https://github.com/tanfy929/Equivariant-I2I":[226]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-16T00:00:00"}
