{"id":"https://openalex.org/W7129526778","doi":"https://doi.org/10.1109/icipw68931.2025.11386333","title":"The Limits of Local Metrics: Investigating the Relationship Between Local Quantization Error and Task Loss in Deep Learning Vision Models","display_name":"The Limits of Local Metrics: Investigating the Relationship Between Local Quantization Error and Task Loss in Deep Learning Vision Models","publication_year":2025,"publication_date":"2025-09-14","ids":{"openalex":"https://openalex.org/W7129526778","doi":"https://doi.org/10.1109/icipw68931.2025.11386333"},"language":null,"primary_location":{"id":"doi:10.1109/icipw68931.2025.11386333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icipw68931.2025.11386333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Image Processing Workshops (ICIPW)","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":null,"display_name":"Ruixiang Chai","orcid":null},"institutions":[{"id":"https://openalex.org/I4210100400","display_name":"Northwestern University","ror":"https://ror.org/00m6w7z96","country_code":"PH","type":"education","lineage":["https://openalex.org/I4210100400"]}],"countries":["PH"],"is_corresponding":true,"raw_author_name":"Ruixiang Chai","raw_affiliation_strings":["Northwestern University,Department of Computer Science"],"affiliations":[{"raw_affiliation_string":"Northwestern University,Department of Computer Science","institution_ids":["https://openalex.org/I4210100400"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126199142","display_name":"Peng Kang","orcid":null},"institutions":[{"id":"https://openalex.org/I79884896","display_name":"University of Illinois at Springfield","ror":"https://ror.org/0126qma51","country_code":"US","type":"education","lineage":["https://openalex.org/I79884896"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peng Kang","raw_affiliation_strings":["University of Illinois Springfield,Department of Computer Science"],"affiliations":[{"raw_affiliation_string":"University of Illinois Springfield,Department of Computer Science","institution_ids":["https://openalex.org/I79884896"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210100400"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.69615669,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"327","last_page":"332"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7878999710083008,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.7878999710083008,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.03610000014305115,"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":0.026599999517202377,"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":0.8154000043869019},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5677000284194946},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.48919999599456787},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4000999927520752},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.3928999900817871},{"id":"https://openalex.org/keywords/monotonic-function","display_name":"Monotonic function","score":0.38960000872612},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.3736000061035156}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.8154000043869019},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6933000087738037},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5871999859809875},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5677000284194946},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.48919999599456787},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4000999927520752},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.3928999900817871},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.38960000872612},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37369999289512634},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.3736000061035156},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3700000047683716},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36070001125335693},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3598000109195709},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.351500004529953},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3287999927997589},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icipw68931.2025.11386333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icipw68931.2025.11386333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Image Processing Workshops (ICIPW)","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":11,"referenced_works":["https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2407386500","https://openalex.org/W2809624076","https://openalex.org/W2963122961","https://openalex.org/W3138516171","https://openalex.org/W3202442802","https://openalex.org/W4312933868","https://openalex.org/W4375868854","https://openalex.org/W4386065704","https://openalex.org/W4406311285"],"related_works":[],"abstract_inverted_index":{"Quantization":[0],"is":[1,67],"widely":[2],"used":[3],"to":[4,133],"reduce":[5],"the":[6,58],"computational":[7],"and":[8,29,40,64,69,74,83,136],"memory":[9],"demands":[10,121],"of":[11,124],"modern":[12],"deep":[13],"neural":[14],"networks,":[15],"enabling":[16],"their":[17],"deployment":[18],"on":[19],"resource-constrained":[20],"edge":[21],"devices.":[22],"Most":[23],"quantization":[24,34,62,97],"methods":[25],"assume":[26],"a":[27,100,129],"simple":[28],"direct":[30],"relationship":[31,59,102],"between":[32,60],"local":[33,61,96],"error":[35,63],"(e.g.,":[36],"layer-wise":[37],"$L_{2}$":[38],"error)":[39],"global":[41],"task":[42,104],"performance":[43,105],"degradation.":[44],"However,":[45],"we":[46,110],"empirically":[47],"demonstrate":[48],"that":[49,112],"this":[50],"assumption":[51],"does":[52],"not":[53],"hold":[54],"in":[55,107],"practice,":[56],"as":[57,86],"task-level":[65,139],"loss":[66],"nonlinear":[68],"varies":[70],"significantly":[71],"across":[72],"layers":[73],"architectures.":[75],"Our":[76],"comprehensive":[77],"empirical":[78,131],"analysis":[79],"covers":[80],"both":[81],"convolutional":[82],"transformer-based":[84],"models,":[85],"these":[87,138],"architectures":[88],"also":[89],"underlie":[90],"many":[91],"multi-modal":[92],"generative":[93],"frameworks.":[94],"Moreover,":[95],"errors":[98],"exhibit":[99],"monotonic":[101],"with":[103],"increase":[106],"most":[108],"cases,":[109],"show":[111],"accurate":[113],"mixed-precision":[114],"bit":[115],"allocation":[116],"requires":[117],"more":[118],"than":[119],"monotonicity-it":[120],"precise":[122],"estimation":[123],"quantization-induced":[125],"loss.":[126],"We":[127],"propose":[128],"practical":[130],"method":[132],"directly":[134],"measure":[135],"estimate":[137],"losses":[140],"efficiently.":[141]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2026-02-18T00:00:00"}
