{"id":"https://openalex.org/W7134852708","doi":"https://doi.org/10.48550/arxiv.2603.07566","title":"GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module","display_name":"GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module","publication_year":2026,"publication_date":"2026-03-08","ids":{"openalex":"https://openalex.org/W7134852708","doi":"https://doi.org/10.48550/arxiv.2603.07566"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.07566","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.07566","pdf_url":null,"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":"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.2603.07566","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068346829","display_name":"Niccol\u00f2 Ferrari","orcid":"https://orcid.org/0000-0002-2578-3211"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ferrari, Niccol\u00f2","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014247231","display_name":"Michele Fraccaroli","orcid":"https://orcid.org/0000-0001-7656-7204"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fraccaroli, Michele","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5028993591","display_name":"Evelina Lamma","orcid":"https://orcid.org/0000-0003-2747-4292"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lamma, Evelina","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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.4235999882221222,"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":0.4235999882221222,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.22699999809265137,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.13819999992847443,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/discriminative-model","display_name":"Discriminative model","score":0.7063000202178955},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6345999836921692},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5809000134468079},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5389000177383423},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5238999724388123},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47999998927116394},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.4307999908924103},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42399999499320984},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.41530001163482666}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7159000039100647},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7063000202178955},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7050999999046326},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6345999836921692},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5809000134468079},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5389000177383423},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5238999724388123},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47999998927116394},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.4307999908924103},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43050000071525574},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.41530001163482666},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.40459999442100525},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3653999865055084},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29660001397132874},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2669000029563904},{"id":"https://openalex.org/C180462255","wikidata":"https://www.wikidata.org/wiki/Q3559736","display_name":"Standard test image","level":4,"score":0.266400009393692},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.07566","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.07566","pdf_url":null,"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":"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.2603.07566","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.07566","pdf_url":null,"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":"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":[{"score":0.7688179612159729,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,228],"is":[2,19,43,49,73,95,136,180],"nowadays":[3],"increasingly":[4],"used":[5,224],"in":[6,68,87,111,189,197],"industrial":[7,88,232],"applications":[8],"and":[9,36,58,80,151,173,216,230],"processes.":[10],"One":[11],"of":[12,16,92,106,170,208,235,239,249],"the":[13,17,20,59,69,77,90,93,155,190,206,250],"main":[14],"fields":[15],"appliance":[18],"visual":[21],"inspection":[22],"for":[23,185],"surface":[24],"anomaly":[25,227],"detection,":[26],"which":[27,72,198],"aims":[28],"to":[29,84,118,148],"spot":[30],"regions":[31,105],"that":[32,47],"deviate":[33],"from":[34,166],"regularity":[35],"consequently":[37],"identify":[38],"abnormal":[39],"products.":[40],"Defect":[41],"localization":[42],"a":[44,52,126,137,144,167,183,244],"key":[45],"task,":[46],"usually":[48],"achieved":[50],"using":[51,182,209],"basic":[53],"comparison":[54],"between":[55],"generated":[56,174],"image":[57,94,159,187],"original":[60],"one,":[61],"implementing":[62],"some":[63,104],"blob-analysis":[64,215],"or":[65,103],"image-editing":[66,217],"algorithms,":[67,211],"post-processing":[70],"step,":[71],"very":[74],"biased":[75],"towards":[76],"source":[78],"dataset,":[79],"they":[81],"are":[82,115,201],"unable":[83],"generalize.":[85],"Furthermore,":[86],"applications,":[89],"totality":[91],"not":[96],"always":[97],"interesting":[98],"but":[99],"could":[100],"be":[101,119],"one":[102],"interest":[107],"(ROIs),":[108],"where":[109],"only":[110],"those":[112],"areas":[113],"there":[114],"relevant":[116],"anomalies":[117,200],"spotted.":[120],"For":[121],"these":[122],"reasons,":[123],"we":[124,223],"propose":[125],"new":[127],"architecture":[128],"composed":[129,169],"by":[130],"two":[131],"blocks.":[132],"The":[133,177,193],"first":[134],"block":[135,157],"Generative":[138],"Adversarial":[139],"Network":[140],"(GAN),":[141],"based":[142],"on":[143],"residual":[145],"autoencoder":[146],"(ResAE),":[147],"perform":[149],"reconstruction":[150],"denoising":[152],"processes,":[153],"while":[154],"second":[156],"produces":[158],"segmentation,":[160],"spotting":[161],"defects.":[162,176],"This":[163,203,241],"method":[164],"learns":[165],"dataset":[168,234],"good":[171],"products":[172],"synthetic":[175],"discriminative":[178],"network":[179,194],"trained":[181],"ROI":[184],"each":[186],"contained":[188],"training":[191],"dataset.":[192],"will":[195],"learn":[196],"area":[199],"relevant.":[202],"approach":[204],"guarantees":[205],"reduction":[207],"pre-processing":[210],"formerly":[212],"developed":[213],"with":[214],"procedures.":[218],"To":[219],"test":[220],"our":[221],"model":[222],"challenging":[225],"MVTec":[226],"datasets":[229],"an":[231],"large":[233],"pharmaceutical":[236],"BFS":[237],"strips":[238],"vials.":[240],"set":[242],"constitutes":[243],"more":[245],"realistic":[246],"use":[247],"case":[248],"aforementioned":[251],"network.":[252]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-11T00:00:00"}