{"id":"https://openalex.org/W4221141812","doi":"https://doi.org/10.1109/icip46576.2022.9898035","title":"Automatic Defect Segmentation by Unsupervised Anomaly Learning","display_name":"Automatic Defect Segmentation by Unsupervised Anomaly Learning","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4221141812","doi":"https://doi.org/10.1109/icip46576.2022.9898035"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9898035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9898035","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2202.02998","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076434285","display_name":"Nati Ofir","orcid":"https://orcid.org/0000-0001-5829-9103"},"institutions":[{"id":"https://openalex.org/I4210165146","display_name":"Applied Materials (Germany)","ror":"https://ror.org/05ejpyv46","country_code":"DE","type":"company","lineage":["https://openalex.org/I193427800","https://openalex.org/I4210165146"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Nati Ofir","raw_affiliation_strings":["Applied Materials"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Materials","institution_ids":["https://openalex.org/I4210165146"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070389543","display_name":"Ran Yacobi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165146","display_name":"Applied Materials (Germany)","ror":"https://ror.org/05ejpyv46","country_code":"DE","type":"company","lineage":["https://openalex.org/I193427800","https://openalex.org/I4210165146"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ran Yacobi","raw_affiliation_strings":["Applied Materials"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Materials","institution_ids":["https://openalex.org/I4210165146"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033899214","display_name":"Omer Granoviter","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165146","display_name":"Applied Materials (Germany)","ror":"https://ror.org/05ejpyv46","country_code":"DE","type":"company","lineage":["https://openalex.org/I193427800","https://openalex.org/I4210165146"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Omer Granoviter","raw_affiliation_strings":["Applied Materials"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Materials","institution_ids":["https://openalex.org/I4210165146"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062084165","display_name":"Boris Levant","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165146","display_name":"Applied Materials (Germany)","ror":"https://ror.org/05ejpyv46","country_code":"DE","type":"company","lineage":["https://openalex.org/I193427800","https://openalex.org/I4210165146"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Boris Levant","raw_affiliation_strings":["Applied Materials"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Materials","institution_ids":["https://openalex.org/I4210165146"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014779621","display_name":"Ore Shtalrid","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165146","display_name":"Applied Materials (Germany)","ror":"https://ror.org/05ejpyv46","country_code":"DE","type":"company","lineage":["https://openalex.org/I193427800","https://openalex.org/I4210165146"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ore Shtalrid","raw_affiliation_strings":["Applied Materials"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Materials","institution_ids":["https://openalex.org/I4210165146"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210165146"],"apc_list":null,"apc_paid":null,"fwci":2.0757,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.85576096,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11338","display_name":"Advancements in Photolithography Techniques","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7710593938827515},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7615677118301392},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7329548597335815},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7202410697937012},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6197502017021179},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5651302337646484},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4561244249343872},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.44624584913253784},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4239109456539154},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.42073068022727966}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7710593938827515},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7615677118301392},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7329548597335815},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7202410697937012},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6197502017021179},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5651302337646484},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4561244249343872},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.44624584913253784},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4239109456539154},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.42073068022727966},{"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icip46576.2022.9898035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9898035","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2202.02998","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.02998","pdf_url":"https://arxiv.org/pdf/2202.02998","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2202.02998","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.02998","pdf_url":"https://arxiv.org/pdf/2202.02998","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W845365781","https://openalex.org/W1159302035","https://openalex.org/W1901129140","https://openalex.org/W1991367009","https://openalex.org/W2007052121","https://openalex.org/W2013927912","https://openalex.org/W2054831422","https://openalex.org/W2115296699","https://openalex.org/W2121927366","https://openalex.org/W2158453800","https://openalex.org/W2412782625","https://openalex.org/W2786599352","https://openalex.org/W2963120456","https://openalex.org/W2963881378","https://openalex.org/W2979548969","https://openalex.org/W2997604048","https://openalex.org/W3100816363","https://openalex.org/W3125440933","https://openalex.org/W3159481202","https://openalex.org/W3163737801","https://openalex.org/W3168822201","https://openalex.org/W3171581326","https://openalex.org/W4210997624","https://openalex.org/W4255391993","https://openalex.org/W6675873586","https://openalex.org/W6774314701","https://openalex.org/W6789968322"],"related_works":["https://openalex.org/W1986655823","https://openalex.org/W4285233543","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W3011384228","https://openalex.org/W2945274617","https://openalex.org/W3199300986","https://openalex.org/W4313052709","https://openalex.org/W4298131179"],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"the":[3,22,45,60,84,89,92,98,101,137],"problem":[4,138],"of":[5,13,21,38,44,62,78,91,100,139],"defect":[6,24,68,129,143],"segmentation":[7,15],"in":[8,83],"semiconductor":[9],"manufacturing.":[10],"The":[11,42],"input":[12],"our":[14,125],"is":[16,56],"a":[17,28,36,76,79],"scanning-electron-microscopy":[18],"(SEM)":[19],"image":[20,81],"candidate":[23],"region.":[25],"We":[26],"train":[27,97],"U-net":[29],"shape":[30],"network":[31,102],"to":[32,116],"segment":[33,117],"defects":[34,119],"using":[35],"dataset":[37,61,126],"clean":[39,63,85],"background":[40,64],"images.":[41],"samples":[43],"training":[46],"phase":[47],"are":[48],"produced":[49],"automatically":[50],"such":[51],"that":[52,72,113],"no":[53,128],"manual":[54],"labeling":[55],"required.":[57],"To":[58,71,87],"enrich":[59],"samples,":[65],"we":[66,74,96,114],"apply":[67,75],"implant":[69],"augmentation.":[70],"end,":[73],"copy-and-paste":[77],"random":[80],"patch":[82],"specimen.":[86],"improve":[88],"robustness":[90],"unlabeled":[93],"data":[94],"scenario,":[95],"features":[99],"with":[103,120],"unsupervised":[104],"learning":[105],"methods":[106],"and":[107,141],"loss":[108],"functions.":[109],"Our":[110,131],"experiments":[111],"show":[112],"succeed":[115],"real":[118],"high":[121],"quality,":[122],"even":[123],"though":[124],"contains":[127],"examples.":[130],"approach":[132],"performs":[133],"accurately":[134],"also":[135],"on":[136],"supervised":[140],"labeled":[142],"segmentation.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}
