{"id":"https://openalex.org/W4399317841","doi":"https://doi.org/10.48550/arxiv.2405.20876","title":"Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study","display_name":"Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study","publication_year":2024,"publication_date":"2024-05-31","ids":{"openalex":"https://openalex.org/W4399317841","doi":"https://doi.org/10.48550/arxiv.2405.20876"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2405.20876","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.20876","pdf_url":"https://arxiv.org/pdf/2405.20876","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2405.20876","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113654355","display_name":"P. Mitra","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mitra, Pallavi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028540603","display_name":"Gesina Schwalbe","orcid":"https://orcid.org/0000-0003-2690-2478"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schwalbe, Gesina","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009563869","display_name":"Nadja Klein","orcid":"https://orcid.org/0000-0002-5072-5347"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Klein, Nadja","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5113654355"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9603000283241272,"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":0.9603000283241272,"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":0.9519000053405762,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9361000061035156,"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/robustness","display_name":"Robustness (evolution)","score":0.7677770853042603},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.614767849445343},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6036913394927979},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.552202045917511},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.512523889541626},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.4622056484222412},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45880237221717834},{"id":"https://openalex.org/keywords/post-hoc","display_name":"Post hoc","score":0.45740464329719543},{"id":"https://openalex.org/keywords/language-change","display_name":"Language change","score":0.41813647747039795},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3454892635345459},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.27452269196510315},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22830483317375183},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1464279294013977},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10182136297225952},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.09871748089790344}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7677770853042603},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.614767849445343},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6036913394927979},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.552202045917511},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.512523889541626},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.4622056484222412},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45880237221717834},{"id":"https://openalex.org/C2992886853","wikidata":"https://www.wikidata.org/wiki/Q18381816","display_name":"Post hoc","level":2,"score":0.45740464329719543},{"id":"https://openalex.org/C2780027415","wikidata":"https://www.wikidata.org/wiki/Q524648","display_name":"Language change","level":2,"score":0.41813647747039795},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3454892635345459},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27452269196510315},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22830483317375183},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1464279294013977},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10182136297225952},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.09871748089790344},{"id":"https://openalex.org/C199343813","wikidata":"https://www.wikidata.org/wiki/Q12128","display_name":"Dentistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2405.20876","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.20876","pdf_url":"https://arxiv.org/pdf/2405.20876","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2405.20876","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2405.20876","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2405.20876","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.20876","pdf_url":"https://arxiv.org/pdf/2405.20876","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4399317841.pdf","grobid_xml":"https://content.openalex.org/works/W4399317841.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W2087343574","https://openalex.org/W4246352526","https://openalex.org/W2121910908"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNNs)":[3],"have":[4],"achieved":[5],"state-of-the-art":[6],"performance":[7,54,101],"in":[8,109],"many":[9],"computer":[10],"vision":[11],"tasks.":[12,115],"However,":[13],"high":[14],"computational":[15],"and":[16,53,73,100,129,137,143],"storage":[17],"demands":[18],"hinder":[19],"their":[20],"deployment":[21],"into":[22],"resource-constrained":[23],"environments,":[24],"such":[25],"as":[26,154],"embedded":[27,139],"devices.":[28],"Model":[29],"pruning":[30,107,121,165],"helps":[31],"to":[32],"meet":[33],"these":[34],"restrictions":[35],"by":[36,156,162],"reducing":[37],"the":[38,91,110,124,157],"model":[39],"size,":[40],"while":[41],"maintaining":[42],"superior":[43],"performance.":[44],"Meanwhile,":[45],"safety-critical":[46],"applications":[47],"pose":[48],"more":[49],"than":[50],"just":[51],"resource":[52],"constraints.":[55],"In":[56],"particular,":[57],"predictions":[58],"must":[59,75],"not":[60,148],"be":[61,76],"overly":[62],"confident,":[63],"i.e.,":[64],"provide":[65],"properly":[66],"calibrated":[67],"uncertainty":[68,71,95,126,141],"estimations":[69],"(proper":[70],"calibration),":[72],"CNNs":[74],"robust":[77,138],"against":[78],"corruptions":[79],"like":[80],"naturally":[81],"occurring":[82],"input":[83],"perturbations":[84],"(natural":[85],"corruption":[86,98,131,145],"robustness).":[87],"This":[88],"work":[89],"investigates":[90],"important":[92],"trade-off":[93],"between":[94],"calibration,":[96,127],"natural":[97,130,144],"robustness,":[99,132],"for":[102,135],"current":[103],"state-of-research":[104],"post-hoc":[105,120,163],"CNN":[106],"techniques":[108],"context":[111],"of":[112],"image":[113],"classification":[114],"Our":[116],"study":[117],"reveals":[118],"that":[119],"substantially":[122],"improves":[123],"model's":[125],"performance,":[128],"sparking":[133],"hope":[134],"safe":[136],"CNNs.Furthermore,":[140],"calibration":[142],"robustness":[146],"are":[147],"mutually":[149],"exclusive":[150],"targets":[151],"under":[152],"pruning,":[153],"evidenced":[155],"improved":[158],"safety":[159],"aspects":[160],"obtained":[161],"unstructured":[164],"with":[166],"increasing":[167],"compression.":[168]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2024-06-04T00:00:00"}