{"id":"https://openalex.org/W7151211984","doi":"https://doi.org/10.1109/icmla66185.2025.00116","title":"Post-training Quantization without BN Statistics: A Data Free Approach","display_name":"Post-training Quantization without BN Statistics: A Data Free Approach","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7151211984","doi":"https://doi.org/10.1109/icmla66185.2025.00116"},"language":null,"primary_location":{"id":"doi:10.1109/icmla66185.2025.00116","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113274418","display_name":"Akash Chavan","orcid":null},"institutions":[{"id":"https://openalex.org/I177721651","display_name":"Oakland University","ror":"https://ror.org/01ythxj32","country_code":"US","type":"education","lineage":["https://openalex.org/I177721651"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Akash Chavan","raw_affiliation_strings":["Oakland University,Department of Computer Science and Engineering,Rochester,MI,USA"],"affiliations":[{"raw_affiliation_string":"Oakland University,Department of Computer Science and Engineering,Rochester,MI,USA","institution_ids":["https://openalex.org/I177721651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072303405","display_name":"S. K. Jha","orcid":"https://orcid.org/0000-0003-4853-6085"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sumit Kumar Jha","raw_affiliation_strings":["University of Florida,Department of Computer and Information Science and Engineering,Gainesville,FL,USA"],"affiliations":[{"raw_affiliation_string":"University of Florida,Department of Computer and Information Science and Engineering,Gainesville,FL,USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014189824","display_name":"Sunny Raj","orcid":"https://orcid.org/0000-0002-7523-1642"},"institutions":[{"id":"https://openalex.org/I177721651","display_name":"Oakland University","ror":"https://ror.org/01ythxj32","country_code":"US","type":"education","lineage":["https://openalex.org/I177721651"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sunny Raj","raw_affiliation_strings":["Oakland University,Department of Computer Science and Engineering,Rochester,MI,USA"],"affiliations":[{"raw_affiliation_string":"Oakland University,Department of Computer Science and Engineering,Rochester,MI,USA","institution_ids":["https://openalex.org/I177721651"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5113274418"],"corresponding_institution_ids":["https://openalex.org/I177721651"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87270738,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"796","last_page":"801"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.33079999685287476,"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"}},"topics":[{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.33079999685287476,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.08259999752044678,"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/T10320","display_name":"Neural Networks and Applications","score":0.06960000097751617,"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/quantization","display_name":"Quantization (signal processing)","score":0.40939998626708984},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.28220000863075256},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.25450000166893005},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.23649999499320984},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.2329999953508377}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4799000024795532},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4555000066757202},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.40939998626708984},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4049000144004822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3190000057220459},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.23649999499320984},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2329999953508377},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.22949999570846558}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla66185.2025.00116","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2145339207","https://openalex.org/W2300242332","https://openalex.org/W2963122961","https://openalex.org/W2981751377","https://openalex.org/W3034940165","https://openalex.org/W3035460915","https://openalex.org/W3097297321","https://openalex.org/W3173567919","https://openalex.org/W3202442802","https://openalex.org/W4313047887","https://openalex.org/W4368358283","https://openalex.org/W4385605073","https://openalex.org/W4410041916"],"related_works":[],"abstract_inverted_index":{"Post-training":[0],"quantization":[1,31,141,189],"(PTQ)":[2],"without":[3,65],"access":[4],"to":[5,44,60,81,88,102,114,175,191],"real":[6],"data":[7,29],"is":[8,79,99],"enabling":[9],"efficient":[10],"model":[11,43,167,173],"optimization":[12],"and":[13,127,168],"deployment":[14],"in":[15,110,148,152,161],"scenarios":[16],"where":[17],"privacy":[18],"or":[19],"proprietary":[20],"constraints":[21],"restrict":[22],"the":[23,40,86,111,116,165,171,176],"use":[24,183],"of":[25,159],"original":[26],"datasets.":[27],"Traditional":[28],"free":[30],"methods":[32],"rely":[33],"on":[34,54,130],"Batch":[35],"Normalization":[36],"(BN)":[37],"statistics":[38,56],"from":[39],"trained":[41],"full-precision":[42],"generate":[45],"a":[46,73],"calibration":[47,74,112],"dataset":[48,75,113],"for":[49,92,106,120,164,170,188],"quantization.":[50,121],"However,":[51],"this":[52,69],"reliance":[53],"BN":[55,66,82],"limits":[57],"their":[58],"applicability":[59],"deep":[61],"neural":[62],"networks":[63],"(DNNs)":[64],"layers.":[67],"In":[68],"paper,":[70],"we":[71,155,182],"propose":[72],"generation":[76],"algorithm":[77],"that":[78,97,136],"agnostic":[80],"statistics,":[83],"leveraging":[84],"just":[85],"backpropagation":[87],"create":[89],"synthetic":[90,186],"images":[91,187],"PTQ.":[93],"We":[94],"also":[95],"demonstrate":[96,135],"it":[98],"not":[100],"necessary":[101],"include":[103],"an":[104,157],"image":[105,132],"every":[107],"target":[108],"category":[109],"get":[115],"representative":[117],"activation":[118],"ranges":[119],"Extensive":[122],"experiments":[123],"with":[124],"both":[125],"large":[126],"lightweight":[128],"models":[129],"large-scale":[131],"classification":[133],"tasks":[134],"our":[137],"method":[138],"consistently":[139],"improves":[140],"performance":[142],"across":[143],"various":[144],"DNN":[145],"architectures,":[146],"especially":[147],"low-bit":[149],"settings.":[150],"Notably,":[151],"4-bit":[153],"quantization,":[154],"achieve":[156],"improvement":[158],"3.31%":[160],"top-1":[162],"accuracy":[163],"ResNet18":[166],"3.82%":[169],"InceptionV3":[172],"compared":[174,190],"state-of-the-art":[177],"(SOTA)":[178],"DSG":[179],"method.":[180],"Importantly,":[181],"very":[184],"few":[185],"other":[192],"methods.":[193]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2026-04-08T00:00:00"}