{"id":"https://openalex.org/W7162809833","doi":"https://doi.org/10.48550/arxiv.2605.29259","title":"KLAS: Using Similarity to Stitch Neural Networks for Improved Accuracy-Efficiency Tradeoffs","display_name":"KLAS: Using Similarity to Stitch Neural Networks for Improved Accuracy-Efficiency Tradeoffs","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162809833","doi":"https://doi.org/10.48550/arxiv.2605.29259"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29259","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29259","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":"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.2605.29259","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023862266","display_name":"Debopam Sanyal","orcid":"https://orcid.org/0000-0002-6761-1389"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sanyal, Debopam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137316547","display_name":"Anantharaman Iyer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Iyer, Anantharaman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108953546","display_name":"Alind Khare","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khare, Alind","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024757120","display_name":"Trisha Jain","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jain, Trisha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137386765","display_name":"Akshay Jajoo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jajoo, Akshay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101640757","display_name":"Myungjin Lee","orcid":"https://orcid.org/0000-0003-2360-7019"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Myungjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052171408","display_name":"Clayton Kerce","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kerce, Clayton","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5048451114","display_name":"Alexey Tumanov","orcid":"https://orcid.org/0009-0005-7862-1477"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tumanov, Alexey","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.5539000034332275,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.5539000034332275,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.2676999866962433,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.028999999165534973,"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/image-stitching","display_name":"Image stitching","score":0.9138000011444092},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.6902999877929688},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6345999836921692},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6043000221252441},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5184000134468079},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4878000020980835},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.4869000017642975},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.47540000081062317},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4530999958515167}],"concepts":[{"id":"https://openalex.org/C29081049","wikidata":"https://www.wikidata.org/wiki/Q1364242","display_name":"Image stitching","level":2,"score":0.9138000011444092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.777400016784668},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.6902999877929688},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6345999836921692},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6055999994277954},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6043000221252441},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5314000248908997},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5184000134468079},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4878000020980835},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.4869000017642975},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.47540000081062317},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4530999958515167},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4424999952316284},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.41940000653266907},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.38359999656677246},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.34290000796318054},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3059999942779541},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29919999837875366},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29259","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29259","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":"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.2605.29259","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29259","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":"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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Given":[0],"the":[1,35,64,100,136,142,160,167,183],"wide":[2],"range":[3],"of":[4,34,60,149,163],"deployment":[5,61],"targets,":[6],"flexible":[7],"model":[8,29,46,125],"selection":[9,116,123],"is":[10],"essential":[11],"for":[12,145],"optimizing":[13],"performance":[14],"within":[15,27],"a":[16,28,50,58,113,191],"given":[17],"compute":[18],"budget.":[19],"Recent":[20],"work":[21],"demonstrates":[22],"that":[23,90,118,157],"stitching":[24,69],"pretrained":[25,45,103,147],"models":[26,104,148,165],"family":[30],"enables":[31],"cost-effective":[32],"interpolation":[33],"accuracy-efficiency":[36,65,93,161],"tradeoff":[37],"space.":[38],"Stitching":[39],"transforms":[40],"intermediate":[41,132],"activations":[42],"from":[43,141],"one":[44],"into":[47],"another,":[48],"producing":[49],"new":[51],"interpolated":[52],"stitched":[53,164],"network.":[54],"Such":[55],"networks":[56],"provide":[57],"pool":[59],"options":[62],"along":[63],"spectrum.":[66],"However,":[67],"existing":[68],"approaches":[70],"often":[71],"yield":[72],"suboptimal":[73],"tradeoffs":[74,94],"and":[75,98,120],"lack":[76],"generalizability,":[77],"as":[78,171],"they":[79],"primarily":[80],"rely":[81],"on":[82],"heuristics":[83],"to":[84,176],"select":[85],"stitch":[86,115,122],"configurations.":[87],"We":[88],"argue":[89],"constructing":[91],"improved":[92],"requires":[95],"explicitly":[96],"capturing":[97],"leveraging":[99,128],"similarity":[101],"between":[102,131],"being":[105],"stitched.":[106],"To":[107],"this":[108],"end,":[109],"we":[110,155],"introduce":[111],"KLAS,":[112],"novel":[114],"framework":[117],"automates":[119],"generalizes":[121],"across":[124],"families":[126],"by":[127],"KL":[129],"divergence":[130],"representations.":[133],"KLAS":[134,158,173],"identifies":[135],"most":[137],"promising":[138],"binary":[139],"stitches":[140],"$O(k^2n^2)$":[143],"possibilities":[144],"$k$":[146],"depth":[150],"$n$.":[151],"Through":[152],"comprehensive":[153],"experiments,":[154],"demonstrate":[156],"improves":[159],"curve":[162],"at":[166,182],"same":[168,184],"finetuning":[169],"cost":[170],"baselines.":[172],"achieves":[174],"up":[175],"$1.21\\%$":[177],"higher":[178],"ImageNet-1K":[179],"top-1":[180],"accuracy":[181,189],"computational":[185],"cost,":[186],"or":[187],"maintains":[188],"with":[190],"$1.33\\times$":[192],"reduction":[193],"in":[194],"FLOPs.":[195]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}
